Anupam Shukla

In India, over 25,000 people have died from cardiovascular annually over the past 4 years , and over 28,000 in the previous 3 years. Most of the deaths nowadays are mainly due to cardiovascular diseases (CVD). Arrhythmia is the leading cause of cardiovascular mortality. Arrhythmia is a condition in which the heartbeat is abnormally fast or slow. The current detection method for diseases is analyzing by the electrocardiogram (ECG), a medical monitoring technique that records heart activity. Since actuations in ECG signals are so slight that they cannot be seen by the human eye, the identification of cardiac arrhythmias is one of the most difficult undertakings. Unfortunately, it takes a lot of medical time and money to find professionals to examine a large amount of ECG data . As a result, machine learning-based methods have become increasingly prevalent for recognizing ECG features. In this work, we classify five different heartbeats using the MIT-BIH arrhythmia database . Wavelet self-adaptive thresholding methods are used to first denoise the ECG signal. Then, an efficient 12-layer deep 1D Convolutional Neural Network (CNN) is introduced for better features extraction, and finally, SoftMax and machine learning classifiers are applied to classify the heartbeats. The proposed method achieved an average accuracy of 99.40%, precision of 98.78%, recall of 98.78%, and F1 score of 98.74%, which clearly show that it outperforms with the exiting model . Architecture of proposed work is simple but effective in remote cardiac diagnosis paradigm that can be implemented on e-health devices.

Millions of people around the world are affected by arrhythmias, which are abnormal activities of the functioning of the heart. Most arrhythmias are harmful to the heart and can suddenly become life-threatening. The electrocardiogram (ECG) is an important non-invasive tool in cardiology for the diagnosis of arrhythmias. This work proposes a computer-aided diagnosis (CAD) system to automatically classify different types of arrhythmias from ECG signals. First, the auto-encoder convolutional network (ACN) model is used, which is based on a one-dimensional convolutional neural network (1D-CNN) that automatically learns the best features from the raw ECG signals. After that, the support vector machine (SVM) classifier is applied to the features learned by the ACN model to improve the detection of arrhythmic beats. This classifier detects four different types of arrhythmias, namely the left bundle branch block (LBBB), right bundle branch block (RBBB), paced beat (PB), and premature ventricular contractions (PVC), along with the normal sinus rhythms (NSR). Among these arrhythmias, PVC is particularly a dangerous type of heartbeat in ECG signals. The performance of the model is measured in terms of accuracy, sensitivity, and precision using a tenfold cross-validation strategy on the MIT-BIH arrhythmia database. The obtained overall accuracy of the SVM classifier was 98.84%. The result of this model is portrayed as a better performance than in other literary works. Thus, this approach may also help in further clinical studies of cardiac cases.

There are two major challenges in mobile sink based wireless sensor networks (WSNs) concerning to the query-driven scenarios, first, dissemination of queries to their respective region of interests (RoIs), and second, routing the data towards mobile sink. Due to sink mobility, routing of data packets to the sink becomes difficult because sink's query injection location and data collection location (current location of sink) may not be the same. Moreover, advertising mobile sink's location by flooding, introduces extensive burden on sensor nodes. In this paper, we propose a virtual ring infrastructure based query-driven ring routing protocol (QRRP) to reduce the overhead of updating mobile sink location information as well as routing the data towards current location of the sink. QRRP takes the advantage of proposed angle based routing to route the queries from mobile sink to their respective RoIs, and data from sensor nodes to the sink. Simulation results on the proposed approach show that energy consumption and data delivery delay are significantly reduced as compared to state-of-the-art mechanisms.

Ventricular Assist Device (VAD) is considered to be the part and parcel to those people who have cardiac complications or heart failure especially the aged patients. Although VADs have contributed remarkably for the past few years, yet these devices possess some limitations, mainly the driveline infections. Due to these conditions, researchers are aiming to improve its functionality as well as other necessary/additional features and hence there is a need to develop the ‘next generation' wireless VAD system which could be very effective to reduce the risk of this infection. In this chapter, the necessity of the VAD and different kinds of VADs are presented and discussed. These features incorporate hemodynamic states after receiving the VADs, selection of biomaterials for the VAD system, VAD pumps and its classifications. Finally, a brief discussion is also provided based on the recent advancement of the VAD system and the scope for the future research.