Cloud-based cardiac health monitoring using event-driven ECG processing and ensemble classification techniques

The functionality of the proposed framework is verified using the MIT-BIH Arrhythmia database [39]. A collection of 24 h outpatient ECG recordings is utilized in this analysis. The dataset contains arrhythmias which are clinically significant. The signal is band-limited to 60 Hz and is captured at a sampling frequency of 360 Hz with standard 11 bit resolution analog-to-digital conversion (ADC). Every ECG recording considered is divided into short time length windows. The windows lengths are selected as equal to 0.9 s. In order to study the performance of the proposed method in terms of identifying various categories of heart pulses, ECG recordings of five different categories are employed. The categories considered are right bundle branch block (RBBB), normal (N), premature ventricular contraction (PVC), left bundle branch block (LBBB) and atrial premature contraction (APC). A total of 1500 ECG instances are considered with 300 instances considered for each of the five classes. This equal representation of each class avoids any possible bias during the classification process.

In the proposed system, an analog bandpass filter is used to filter the incoming analog ECG signal y(t). The band-limited signal, x(t), is conveyed to an event-driven analog-to-digital converter (EDADC). The traditional ECG based cardiovascular system disorder identification and diagnosis systems are founded on the basis of the Nyquist theory. They utilize traditional ADCs and record and process the ECG signals at a fixed rate. Therefore, the choice of processing rate is made for the worst possible case. As a result, these solutions are not efficient, which is particularly true in the case of time-varying intermittent signals such as ECG. These disadvantages can be mitigated by embedding signal-piloted EDADCs in the system [37, 40]. The functional mechanism of EDADCs is completely different to traditional ADCs. EDADCs do not follow the Nyquist sampling criterion and the acquisition of a new sample is not triggered by a fixed rate clock. Instead, they are based on the mechanism of level crossing sampling (LCS) and respect the Bernstein sampling theorem [37]. The sampling acquisition is triggered by the phenomenon of threshold crossing. A new sample is acquired once the band-limited analog signal x(t) traverses any of the preset threshold levels. As a result, the incoming analog signal adapts the system's acquisition activity and sampling rate.

The signal discretized with LCS is repartitioned in a non-uniform time frame. The instants of sampling are defined by

$$\begin{eqnarray}&&{t}_{n}={t}_{n-1}+{\rm{d}}{t}_{n},\end{eqnarray} \tag{ 11.1 }$$

where t_n is the present sampling instant, t_n−1 is the prior instant, and dt_n is the time difference between the present and the prior sampling instants [41]. For a selected EDADC amplitude dynamics, ΔV, and resolution, M, its quantum, q, can be calculated as $q=\frac{\Delta V}{{2}^{M-1}}$ [37, 39, 42]. The EDADC focuses on the relevant ECG information and avoids the unwanted information. They obtain a smaller number of samples relative to the classical equivalents. This greatly decreases the operations of the post-processing and transmission modules and achieves power-efficient implementation.

The EDADC output is segmented by the activity selection algorithm (ASA) [41]. It utilizes the non-uniformity of the sampling method to select only the important sections of the signal. The traditional windowing functions [40, 41] do not offer suitable features for the ASA. Only the necessary information of the signal is selected. Furthermore, the form and length of the window function are modified according to the local characteristics of the windowed signal [37, 41]. In addition, an efficient reduction of the phenomenon of leakage is also achieved [41].

For a given resolution M, the EDADC sampling rate changes with the temporal variations of x(t). The highest sampling frequency of EDADC [37] is determined by

$$\begin{eqnarray}&&F{s}_{\max }=2\cdot {f}_{\max }.({2}^{M}-1)\cdot \frac{{A}_{\mathrm{in}}}{{\rm{\Delta }}V},\end{eqnarray} \tag{ 11.2 }$$

where f_max is the x(t) highest frequency component, A_in is the input signal amplitude and Fs_max is the maximum sampling frequency of the EDADC. Let Wⁱ represent the ith chosen segment which is produced by the ASA. If Fsⁱ is the frequency of sampling for Wⁱ , then mathematically it can be computed by the relation Fsⁱ = Nⁱ /Lⁱ . Lⁱ is measured in seconds and it is the time length of Wⁱ . The count of samples which exist in Wⁱ is denoted by Nⁱ . There are plenty of mature and sophisticated signal treatment tools which are based on the traditional sampling mechanism [40, 41]. Therefore, to benefit from such methods and to integrate them in the suggested solution, each Wⁱ is resampled uniformly. The resampling frequency is tuned by following the locally extracted parameters [41]. It resamples and processes the selected signal sections at the same or lower rates compared to to the conventional equivalents. Consequently, the proposed solution's computational advantage is greatly improved relative to conventional counterparts.

Let Frsⁱ represents the resampling rate for the selected segment Wⁱ ; then Frsⁱ is chosen specifically for each Wⁱ . A reference rate of sampling is selected in the system and is denoted as F_r . This Frsⁱ selection mechanism is a function of F_r and Fsⁱ . Then, Wⁱ is resampled at a rate of Frsⁱ and this results in a new count of samples, namely Nrⁱ . The online resampling is obtained using interpolation. The interpolation method updates the resampled signal characteristics with respect to the original signal [43]. For the devised method, the interpolation error depends on ΔV, M and the interpolation technique used [43].

Qaisar et al [43] showed that for the case of the EDADC, the maximum resampling error is bounded by q. However, it could be equal to ΔV in the traditional counterparts. This demonstrates the appropriateness of using quick and low-complexity interpolation approaches, rather than the conventional equivalents, in our proposed method. In addition, this study focuses on increasing the computational performance of wearable ECG devices and cloud-based computing. Consequently, the simplified linear interpolation (SLI) approach is considered for online resampling of the segmented signal parts. In SLI the value of xr_n is set equal to the mean of its prior and next non-uniform samples and the highest error per interpolation is limited by $\frac{{q}}{{2}}$ [43].

It is necessary to condition the biomedical signals to reduce the impact of artifacts and noise. The conditioning process is frequently achieved by utilizing digital filters [44]. Traditional digital filtering is static in nature. The sampling rate is kept fixed and the digitized signal is conditioned using a fixed order filter. Therefore, the parameters, such as sampling rate and the order of filters, are selected for the worst possible case [45]. Such a choice could result in a remarkable but unnecessary increase in the processing load and power consumption of the conditioning stage. This performance limitation can be improved by using multirate processing concepts [46]. In this context, novel techniques of digital filtering are proposed which can adjust their rate of conditioning and the order of filter according to the incoming signal changes [47].

Keeping in mind the targeted application, an appropriate bank of reference digital filters is realized offline. The implementation is achieved for a set of reference sampling frequencies, denoted as Fref. Different elements of the set Fref are computed by utilizing a fixed step of frequency, denoted as Δ. This leads towards a uniformly spaced placement of various elements of the set Fref. The first element in the set Fref is Fs_min ⩾ 2F_Cmax and it defines the lower limit on the elements of set Fref. It assures an effective digital filtering process. The last element in the set Fref is F_r and it defines the upper bound on the elements of the set Fref.

For each selected segment one appropriate predesigned digital filter is selected during the real-time online signal conditioning. This selection is a function of the values of various elements of the set Fref and the real-time calculated value of Fsⁱ . Depending on the signal variations, the value of Fsⁱ can be specified and therefore an appropriate mechanism of the reference filter choice is adopted to obtain an effective and efficient denoising. For the case when Fsⁱ ⩾ F_r , the offline designed reference filter built for F_r is used for Wⁱ . However, if Fsⁱ < F_r , then the reference filter whose corresponding value of Fref_c , which is neighboring the Fsⁱ is selected for Wⁱ . Here, the index 'c' represents the real-time selected element of the set Fref for online conditioning of Wⁱ .

This adaptation of Frsⁱ causes Wⁱ to be resampled closer to the Nyquist rates or at sub-Nyquist rates [47]. Hence, during data resampling and denoising procedures, it prevents unwanted interpolations and filtering operations. As a result, the proposed solution increases the computational and power efficiencies.

The interesting features of the denoised signal are extracted using the autoregressive (AR) Burg method [15]. In this case, xr_n is modeled as the output of an all-pole causal filter. The method for an Oth order model can be presented as

$$\begin{eqnarray}&&{z}_{n}=\sum _{j=1}^{O}{a}_{j}x{r}_{n-j}+{b}_{n},\end{eqnarray} \tag{ 11.3 }$$

where xr_ni is the input, a_j are the model's weighing coefficients and b_n is the variance of the white noise ${\sigma }^{2}$. An Oth order model could estimate the power spectral density (PSD) using the following equation, where F_s is the sampling frequency:

$$\begin{eqnarray}&&{P}_{\mathrm{AR}\mathrm{Burg}}(\,f)=\frac{{\sigma }^{2}}{{\left|1+\sum _{j=1}^{O}{a}_{j}\cdot {{\rm{e}}}^{\frac{-i2\pi fj}{{F}_{s}}}\right|}^{2}}.\end{eqnarray} \tag{ 11.4 }$$

The technological advancements in computing and electronics have evolved the utilization of machine learning approaches in a variety of applications in our modern era. The same trend is being followed in the domain of biomedical signal processing. A variety of mature machine learning algorithms have been introduced for the classification and diagnosis of biomedical signals. Certain practical examples are the identification of myocardial ischemia, epilepsy, heart attacks and cardiac hypertrophy. Such an approach is particularly beneficial in the case of emergencies. It can accelerate the process of analysis and decision making and can lead towards timely and effective treatment of the patient. Accurate and timely care measures can be taken through effective use of sophisticated machine learning approaches. Currently, various treatment approaches are being considered by medical practitioners. Certain common examples are electrocardiography, electroencephalography, magnetic resonance imaging and tomographic scanners [48].

Real-time and uninterrupted biomedical signal analysis is very important for tracking the health conditions of patients with critical conditions. The overall process consists of different stages of signal acquisition, conditioning, analysis and classification. It involves a smart combination of a variety of sophisticated techniques and technologies. An effective diagnosis can be realized by extracting pertinent features from the acquired and conditioned biomedical signals. This makes it convenient to identify the divergence of the intended signal from its standard parameters. The monitoring process should be robust and accurate in identifying the type of abnormality occurring. In addition, the doctor should be well trained in interpreting the detected abnormality as an accurate health disorder. Manual analysis of such data needs a lot of training and experience. This is very difficult, in particular in the case of big and multidimensional health data. Therefore, computer based machine learning methods can contribute a great deal in this respect [48].

Machine learning techniques may automate the process of biomedical signal analysis and classification between normal and pathological patterns by producing decisions to classify these patterns. A crucial feature of clinical surveillance has been the automated identification and recognition of physiological signals using various signal processing techniques [48]. Because biomedical signals provide such a large amount of data, the main problem for classification is how to classify the biomedical signal records. Next, the relevant features must be derived from the acquired biomedical signals, then the size of this feature set must be decreased and in next step the decreased size feature set is employed in the final classification stage. An algorithm that implements classification is called a classifier. A classifier knows how to use training sets to categorize the class of a function variable. The algorithms used for predicting categorical labels in the classification process are the k-nearest neighbors (k-NN), artificial neural network (ANNs), support vector machine (SVM), classification and regression tree (CART), random tree, REP tree, random forest and LAD tree [49] classifiers.

The artificial neural network (ANN) is a set of linked input and output units. Each link can have a specific weight. The learning is accomplished by adjusting these weights. It allows proper classification of test data. The ANN is inherently parallel in nature. Therefore, parallelization approaches can be used to speed up the classification [50]. The support vector machine (SVM) performs a nonlinear projection to maximize the dimension of the training data. It enables the most effective classifier to be found and can accurately differentiate the test data for each class. k-nearest neighbors (k-NN) learns and describes the test sample by contrasting it to the testing samples. k training instances are used to classify the considered instance. The decision is based on distance calculation. The classification and regression tree (CART) recognizes statistical reference variables, regression, but does not calculate rule sets. It constructs binary trees using the function and threshold that generate the maximum intelligence at each node. The REP tree develops a tree of prediction to manage the missing labels by splitting the given dataset into sections. The test set is defined by using models with a similarity exclusion effect applied to the nodes [50]. The random tree is based on the principle of an ensemble learning approach which generates distinct learners. A random set of data is generated by employing the bagging technique. It is later employed to raise the decision tree. Its classification principle is to pass the input variables to a collection of predictors. The classification vote of each predictor is employed to make the final decision. The label of the class is decided on the basis of the majority of votes. The LAD tree seeks to characterize data by splitting it with a linear plane, which implies that the data are generated independently from two different normal distributions with equivalent co-variance models for each group. To have different means, it finds the best position to place a line between the two distributions. It allows minimizing the approximation error. The random forest is based on a combination of predictors for a tree. The tree is based on values of a random vector which is sampled independently. Nonetheless, for all trees in the forest a common distribution is practiced. The classification error is a function of the accuracy and the connotation between different trees [50].

An ensemble of robust classifiers is used in the ensemble learning approaches. Such an approach is beneficial in terms of classification precision and can outperform single classifier based methods. It is a frequently used technique in a variety of machine learning domains. Recent technological advancements have expanded the use of ensemble classification approaches in biomedical applications. Ensemble classifications centering on the completely complex-valued networks of neural relaxation have been extended to the classification of images. They apply the same classification problem to many classifiers. Classifier tests with different accuracy ratings are paired with various methods such as average and voting in this form of study. Therefore, better predictive results can be obtained compared to a single classifier. In this chapter each considered single classifier, such as k-NN, ANN, SVM, etc, is also used with the bagging, AdaBoost and random subspace models in order to attain a higher arrhythmia classification accuracy [51].

In the case of bagging, different identically sized training datasets are chosen at random from the broader problem domain. Then using a different machine learning method, a decision tree can be created for each dataset. These trees may be expected to be identical in substance and to enforce the same classification for each new test case. This hypothesis, however, is usually incorrect, particularly if the training datasets are very small, as induction of the decision tree is not a stable process. Small changes in the training data therefore simply generate a different attribute that is selected at a specific node, with significant consequences for the subtree structure under that node. It shows that there are certain instances where multiple decision trees produce specific forecasts and others do not. Bagging attempts to balance the uncertainty of learning methods by simulating the procedure described before using a given training set, instead of obtaining independent domain datasets [52]. AdaBoost can be utilized for some bagging-like learning. To simplify the method, the learning algorithm is presumed to be able to modify weighted instances that are positive numbers. The presence of instance weight affects the way an error is measured for a classifier. Typically, in this case it is determined by dividing the weights accumulated by the cumulative weights of all instances into the misclassified instances. The weighting of instances allows the learning algorithm to concentrate on a given set of higher weighted instances. Such higher weighted instances are more critical than others as there is a greater inducement to identify them correctly [52]. Random subspace is an ensemble learning approach that aims to reduce the correlation among estimators in an ensemble by making them conditioned on random samples of features rather than the whole set of features. It is comparable to bagging except that the attributes for each learner are collected randomly, with substitution. This informally allows individual learners not to over-focus on features that tend to be incredibly predictive in the training set, but fail to be as reliable for points beyond that set. Random subspace is thus an appealing alternative for problems where the number of features is much greater than the number of training points.

One criterion is to measure the system output in terms of the gain in compression. Classically, x(t) is captured at a fixed rate. The total sample count for a given time period ${{L}}_{{T}}$ is obviously calculated as $N={F}_{\mathrm{ref}}\times {L}_{T}$. The discrepancies of the incoming signal tune the sampling rate of the EDADC [37]. When the signal is smoother the sampling rate remains low and when the signal varies rapidly the sampling rate increases accordingly. This is the reason behind the change in the collected number of samples for each selected segment, even if the temporal length of each segment is kept equal to L_T . The sampling density of the EDADC also depends on the resolution of the EDADC alongside the characteristics of the incoming signal [37]. Let us suppose that the designed approach collects N_ED samples for the time length of L_T . Then, it is straightforward to calculate the compression ratio, R_COMP. It can be computed by calculating the ratio between N and N_ED. This process can be mathematically described as

$$\begin{eqnarray}&&{R}_{\mathrm{COMP}}=\frac{N}{{N}_{\mathrm{ED}}}.\end{eqnarray} \tag{ 11.5 }$$

The computational complexity of the front-end processing module is studied in depth up to the denoising stage. The complexity of the feature extraction and classification modules is evaluated at an intuitive level by taking into account the compression ratio of the proposed solution. The adaptive rate FIR (ARFIR) method is utilized for denoising the resampled signal [41].

The numerical complexity of a classical K order FIR filter is well known, and the entire computational complexity C_FIR can be measured using

$$\begin{eqnarray}&&{C}_{\mathrm{FIR}}=\mathop{\underbrace{K\cdot N}}\limits_{\mathrm{Additions}}+\mathop{\underbrace{K\cdot N}}\limits_{\mathrm{Multiplications}}.\end{eqnarray} \tag{ 11.6 }$$

The online adaptation requires additional operations in the case of an invented solution. First, a reference filter hc_k is selected for Wⁱ . This real-time filter selection is performed using the successive approximation algorithm. It performs log₂(Q) comparisons for the worst case. Here, Q is the length of set Fref, in other words it is the count of elements in this set. It is also necessary to resample the selected segments at the real-time computed resampling rate Frsⁱ . The live resampling is achieved by using the SLI. For SLI, the count of operations for each Wⁱ is Nrⁱ additions and Nrⁱ binary weighted divisions. The binary weighted divisions are realized by employing the one-bit right shift mechanism. The complexity of such an implementation is insignificant in contrast to the addition and multiplication operations [53]. With this background, the computational cost of SLI is approximated as Nrⁱ additions for Wⁱ . After uniform resampling, the Wⁱ is conditioned by utilizing a Kⁱ th order digital filter. The count of operations during the conditioning process is Kⁱ ·Nrⁱ multiplications and Kⁱ Nrⁱ additions for Wⁱ . The complexity of a comparison operation is assumed to be equal to that of an addition operation. This assumption allows us to evaluate the processing cost of the devised ARFIR with respect to that of the classical one. In this context the computational complexity of the ARFIR can be represented as

$$\begin{eqnarray}&&{C}_{\mathrm{ARFIR}}=\mathop{\underbrace{{K}^{i}\cdot N{r}^{i}+N{r}^{i}+{\mathrm{log}}_{2}\left(Q\right)}}\limits_{\mathrm{Additions}}+\mathop{\underbrace{{K}^{i}\cdot N{r}^{i}}}\limits_{\mathrm{Multiplications}}.\end{eqnarray} \tag{ 11.7 }$$

The pertinent parameters of the denoised signal are extracted using the AR Burg method. Several robust classification algorithms use these derived parameters to identify the ECG categories. The effectiveness of the entire chain is evaluated in terms of the classification performance. A limited collection of data causes difficulties in estimating classification results and a cross-validation scheme is often used in this situation [50]. Therefore, this analysis utilizes the ten-fold cross-validation. For each classifier, the accuracy, F-measure, ROC field and Kappa values are evaluated using the same test set.

Accuracy is defined as the ratio of properly categorized instances against all instances and is given as

$$\begin{eqnarray}&&\mathrm{Accuracy}=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{FP}+\mathrm{FN}+\mathrm{TN}},\end{eqnarray} \tag{ 11.8 }$$

where TP is true positives, TN is true negatives, FP is false positives and FN is false negatives.

The F-measure, defined as the harmonic mean of the accuracy and recall indicators, can be calculated as

$$\begin{eqnarray}&&{F}\unicode{x02010}\text{measure}=\frac{2\times \mathrm{TP}}{2\times \mathrm{TP}+\mathrm{FP}+\mathrm{FN}}.\end{eqnarray} \tag{ 11.9 }$$

Receiver operating characteristic (ROC) analysis is one of the helpful tools that allows the evaluation of classifiers at multiple operating points, the collection of operating points and the correlation of operating points. The ROC curve displays the full range of different operating points in a single graph, with the corresponding varying rates of the true positives and false positives.

The Kappa statistic is a metric which takes into account the predicted figure by deducing it from the successes of the predictor. For a good predictor it expresses the outcome as a proportion of the sum. Kappa statistics are used to calculate the agreement between expected and actual classifications of a dataset, and to correct the agreement that occurs by chance.

The efficiency of the proposed solution is studied for the collection, processing and classification of the MIT-BIH arrhythmia dataset signals. In the classical case, each considered record is divided into fixed length windows of 0.9 s length. The signal is recorded at a fixed sampling rate of 360 Hz. Therefore, it results in 330 samples for each window. In the case of the suggested approach, the ECG signal is acquired with an EDADC of M = 5. Since the ECG signals are band-limited by f_max = 60 Hz, the maximum EDADC sampling frequency is Fs_max = 3.72 kHz (see equation (11.2)).

The output of EDADC is irregularly spaced in time. Therefore, it cannot be segmented by utilizing the standard windowing operations. In this context, the ASA is utilized for segmenting the EDADC output. The reference window length L_ref is chosen as equal to 1 s [41]. L_ref is tuned for each selected segment by the ASA. It mainly allows focusing on and treating the active signal portion while neglecting the unwanted and redundant baseline. This activity selection process augments the processing gain and the power effectiveness of the suggested method. By extracting the parameters of each selected segment the ASA permits adjusting the sampling frequency in a real-time manner and the denoising stage filter order as a function of the incoming signal disparities. This allows the process to concentrate only on the interesting sections of the signal and achieves a reduced number of samples for each selected window, in contrast to the traditional method. However, in the case of typical equivalents, despite incoming signal variations, the window length and the count of samples per window remain identical, 0.9 s and 330 samples, respectively. Consequently, this induces an increase in the processing and power efficiencies of the suggested approach.

For all 300 instances of each class the average compression ratios are determined using equation (11.8). These are 2.76, 2.25, 2.59, 2.61 and 2.69, respectively, for classes N, RBBB, LBBB, APC and PVC. The average compression gain obtained overall for all five classes is 2.6-fold.

The ASA's output is resampled uniformly using the SLI, and is conditioned using the ARFIR. A bank of bandpass filters is built offline. The cut-off frequencies of each filter are [F_Cmin = 0.5; F_Cmax = 30] Hz. It enables the emphasis to be on the ECG band of interest while attenuating the undesirable parts. It results in an increased precision of the extraction and classification modules for the post applications. The filter bank is implemented for a range of sample frequencies, Fref, between 76.25 Hz > 2F_Cmax to F_r = 320 Hz. In this case Δ = 32.5 Hz is chosen. It realizes a bank of Q = 8 bandpass FIR filters. A description of the bank of filters is shown in table 11.1.

Depending on the incoming signal variations and the employed EDADC resolution, the real-time calculated resampling frequency, Frsⁱ , could be specific for each Wⁱ and it is aligned in a real-time manner with the chosen reference filter's sampling frequency, Fref_c . The conditioning stage improves the acquired signal's signal to noise ratio (SNR) and allows us to achieve an improved classification performance [12, 13]. The employed adaptive rate denoising tactic obtains the conditioning of the signal with a reduced number of operations in contrast to the traditional counterparts [41]. The average reductions, compared to the classical method, in the number of operations of the devised ARFIR are calculated for all 300 instances of each class considered. In terms of additions these are, respectively, 5.95, 4.62, 5.47, 5.64 and 5.93 for classes N, RBBB, LBBB, APC and PVC. In terms of multiplications these are, respectively, 6.08, 4.71, 5.59, 5.77 and 6.06 for classes N, RBBB, LBBB, APC and PVC. The average gain for all five classes is also computed. The gain in the additions and multiplications are 5.52-fold and 5.64-fold, respectively.

The AR Burg method is employed to extract the discriminative and informative features of the conditioned signal. These attributes are then used for classification. For the classical case, summaries of the classification results obtained using the different classifiers are described in tables 11.2 and 11.3, and the classification results obtained by applying the proposed event-driven acquisition and processing-based approach are presented in tables 11.4 and 11.5.

Table 11.2 shows the results for the five-class ECG dataset in terms of the classification precision and the F-measure values for the classical case. Among the single classifiers, the highest classification accuracy of 95.7% is achieved by the random forest. A minor increase in performance is achieved by the ensemble of random forest with AdaBoost, with an accuracy of 95.8%. Among the single classifiers, the highest F-measure value of 0.957 is also achieved by the random forest. Among the ensemble classifiers, the random forest with AdaBoost secures the highest F-measure value of 0.958. It is apparent that the accuracies of the weak classifiers are improved by the ensemble classifier models.

Table 11.3 shows the results for the five-class ECG dataset in terms of the ROC area and the kappa statistics for the classical sampling case. Among the single classifiers, the highest ROC area value of 0.996 is achieved by the random forest. A similar performance is achieved by the ensemble of random forest with Adaboost and random forest with random subspace. However, a minor performance increase is obtained by the random forest with bagging, achieving an ROC area value of 0.997. Among the single classifiers, the highest kappa value of 0.946 is also achieved by the random forest. Among the ensemble classifiers, the random forest with bagging secures a similar performance. A minor increase is obtained by the random forest with AdaBoost by obtaining a kappa value of 0.948. Moreover, it is clearly seen that the accuracies of the weak classifiers are improved by the ensemble classifier models.

The above results (tables 11.2 and 11.3) show that for the classical case the chain of classical ADC, windowing, FIR filter and AR Burg achieves the best classification performance with the random forest. A minor increase in the performance is achieved by using a smart ensemble of random forest with AdaBoost. In addition, the accuracies of the weak classifiers are improved by the ensemble classifier models.

Table 11.4 shows results for the five-class ECG dataset in terms of the classification precision and the F-measure values for the devised chain. Among the single classifiers, the highest classification accuracy of 94.3% is achieved by the random forest. A similar performance is achieved by the ensembles of random forest with bagging and random forest with AdaBoost. A minor increase is obtained by the ensemble of ANN with random subspace by achieving a precision of 94.5%. Overall, the highest classification accuracy of 94.7% is achieved by the ensemble of random forest with random subspace. Among the single classifiers, the highest F-measure value of 0.943 is achieved by the random forest. A similar performance is achieved by the ensembles of random forest with bagging and random forest with AdaBoost. A minor increase is obtained by the ensemble of ANN with random subspace by achieving an F-measure of 0.945. Overall, the highest F-measure value of 0.947 is secured by the ensemble of random forest with random subspace. In this case, the accuracies of the weak classifiers are improved by the ensemble classifier models as well.

Table 11.5 shows the results for the five-class ECG dataset in terms of the ROC area and the kappa statistics for the devised chain. Among the single classifiers, the highest ROC area value of 0.996 is achieved by the random forest. A similar performance is achieved by the ensembles of random forest with bagging and random forest with AdaBoost. A minor increase is achieved by the ensemble of random forest with random subspace, achieving an ROC area value of 0.997. Among the single classifiers, the highest Kappa value of 0.928 is achieved by the random forest. A similar performance is achieved by the ensemble of random forest with bagging. A minor increase is obtained by the ensemble of random forest with AdaBoost, achieving a kappa value of 0.929. A further increase is achieved by the ensemble of ANN with random subspace, yielding a kappa value of 0.932. Overall, the highest kappa value of 0.933 is achieved by the ensemble of random forest with random subspace. One more point that should be mentioned is again that the accuracies of the weak classifiers are improved by the ensemble classifier models.

The above results (tables 11.4 and 11.5) show that, for the suggested event-driven chain of the EDADC, ASA, ARFIR and AR Burg method, random forest achieves the best classification performance. A minor increase in the performance is achieved using a smart ensemble of random forest with random subspace.

Recent advancements in wireless sensor techniques and technologies enhance the development and deployment of smart implants in general and the wireless and self-powered biomedical implants in particular. A variety of biomedical implants have been introduced in the literature. The remote monitoring and diagnosis of patients is a beneficial concept and is particularly helpful for the remote and rural communities of the developing word. Inspired by its promising benefits this domain of research has attracted many researchers and industries for different potential domains, such as computation, information science and electronics. A recent trend is the integration of cloud-based applications in modern mobile healthcare solutions. By considering the developments and results in the cloud-based mobile healthcare sector, it can be imagined how much this approach will expand and be deployed further in the near future. It will revolutionize the sensor, computer and networking fields of research and development.

Cloud-based biomedical signal processing for the automatic diagnosis of health condition monitoring is a novel trend. It is evident that it is going to play an important role in future mobile healthcare solutions. One key application is to automatically detect cardiovascular system disorders to avoid any possible heart failures. One of the aims of this chapter is to reflect many of these developments and also to recognize other essential research issues in the mobile healthcare framework. It is based on smart wearables that are linked to cloud-based applications.

The benefits of the proposed method are evident from the results. It employs an intelligent combination of the event-driven acquisition and the adaptive rate processing mechanisms. It is capable of adjusting Lⁱ , Frsⁱ and Nⁱ as a function of the incoming signal variations. This ensures the dynamic parameter adaptation of the suggested solution in accordance with the incoming signal disparities. It is shown how the adaptation of Lⁱ and Nⁱ automates the real-time tuning of Frsⁱ . This real-time adjustment of Frsⁱ enhances the processing effectiveness and the power efficiency of the designed approach in contrast to the traditional equivalent. It is obtained by omitting needless operations while performing the real-time and online interpolation of the active signal portions. It also avoids the collection and conditioning of irrelevant information and leads towards an effective denoising mechanism. Additionally, the real-time adoption of filter orders also adds to the effectiveness of the suggested conditioning mechanism [11].

One crucial function is the consistent and effective mobile supervision of chronically ill patients. In this chapter we developed an event-driven and adaptive rate cloud-based remote health monitoring system. It utilizes ECG signals for arrhythmia categorization. The primary objective of this chapter is to illustrate how wireless biomedical instruments can be used to anticipate and detect chronic disorders in real time, using an automated, smart and scalable cloud-based mobile healthcare monitoring framework. The suggested approach was checked as being practical, accurate and successful in the measurement and evaluation of cardiac health monitoring. The advances in mobile device and sensor technologies include innovative ways to improve the quality of care for patients with chronic disorders. The proposed approach is new and has the ability to improve healthcare technology and medical systems for the coming age.