End-to-End Deep Learning for Reliable Cardiac Activity Monitoring using Seismocardiograms

Suresh, P.; Pranav, Chakilam Vijay; Vijayaraghavan, Vineeth

doi:10.1109/icmla51294.2020.00213

Cited by 13 publications

(7 citation statements)

References 23 publications

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“…In [133], an SCG-based approach was introduced to identify the abnormality in the aortic flow. In [134], a Deep Convolutional Neural Network (D-CNN)-based approach was proposed for robustly monitoring the cardiac activity from SCG signals. In [135], SCG was considered for monitoring the left ventricular function of the cancer patients using the assessment of cardiotoxicity.…”

Section: Other Applicationsmentioning

confidence: 99%

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

et al. 2021

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In recent years, cardiovascular diseases are on the rise, and they entail enormous health burdens on global economies. Cardiac vibrations yield a wide and rich spectrum of essential information regarding the functioning of the heart, and thus it is necessary to take advantage of this data to better monitor cardiac health by way of prevention in early stages. Specifically, seismocardiography (SCG) is a noninvasive technique that can record cardiac vibrations by using new cutting-edge devices as accelerometers. Therefore, providing new and reliable data regarding advancements in the field of SCG, i.e., new devices and tools, is necessary to outperform the current understanding of the State-of-the-Art (SoTA). This paper reviews the SoTA on SCG and concentrates on three critical aspects of the SCG approach, i.e., on the acquisition, annotation, and its current applications. Moreover, this comprehensive overview also presents a detailed summary of recent advancements in SCG, such as the adoption of new techniques based on the artificial intelligence field, e.g., machine learning, deep learning, artificial neural networks, and fuzzy logic. Finally, a discussion on the open issues and future investigations regarding the topic is included.

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Section: Other Applicationsmentioning

confidence: 99%

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

et al. 2021

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“…They achieved a precision (positive predictive value) and recall rate (sensitivity) that exceeded the average cardiologist performance. In recent years, different works including wide range of medical tasks have used Deep Neural Network (DNN) for classification and prediction purposes [44][45][46]. DNN has shown a significant effect on the accuracy of classification and provided continuous monitoring of the heart conditions and arrhythmia of a captured ECG signal with low cost and improved prediction quality.…”

Section: Introductionmentioning

confidence: 99%

“…DNN has shown a significant effect on the accuracy of classification and provided continuous monitoring of the heart conditions and arrhythmia of a captured ECG signal with low cost and improved prediction quality. Recently, various Machine Learning (ML) and Deep Learning (DL) algorithms have been widely used to analyze Seismocardiogram (SCG) signals to get descriptive and predictive information for diagnosis purposes [46]. A three-layer artificial neural network have been proposed by Yao et al [47], it combines both ECG and SCG signals on a beat-by-beat basis for personalized quiescence prediction.…”

Section: Introductionmentioning

confidence: 99%

“…There is a rise in the number of devices that are used in everyday life for monitoring non-medical grade signals, however achieving a reliable analysis of these signals is still a challenging task. The gap between a non-clinical signal acquisition and a reliable signal analysis can be bridged by proposing an end-to-end deep learning network that could extracts information from the prevalent SCG signals in robust manner [46].…”

Section: Introductionmentioning

confidence: 99%

“…Suresh et al [46] proposed SeismoNet, a Deep Fully Convolutional Neural Network to accurately detects R-peaks for robust monitoring of the SCG signal. SeismoNet is trained in an end-to-end manner to detect position of the R-peaks in spite of noise.…”

Section: Introductionmentioning

confidence: 99%

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In this paper we have utilized a hybrid lightweight 1D deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods for accurate, fast, and automated beat-wise ECG classification. The CNN and LSTM models were designed separately to compare with the hybrid CNN-LSTM model in terms of accuracy, number of parameters, and the time required for classification. The hybrid CNN-LSTM system provides an automated deep feature extraction and classification for six ECG beats classes including Normal Sinus Rhythm (NSR), atrial fibrillation (AFIB), atrial flutter (AFL), atrial premature beat (APB), left bundle branch block (LBBB), and right bundle branch block (RBBB). The hybrid model uses the CNN blocks for deep feature extraction and selection from the ECG beat. While the LSTM layer will learn how to extract contextual time information. The results show that the proposed hybrid CNN-LSTM model achieves high accuracy and sensitivity of 98.22% and 98.23% respectively. This model is light and fast in classifying ECG beats and superior to other previously used models which makes it very suitable for embedded systems designs that can be used in clinical applications for monitoring heart diseases in faster and more efficient manner.

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End-to-End Deep Learning for Reliable Cardiac Activity Monitoring using Seismocardiograms

Cited by 13 publications

References 23 publications

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

Heart Rate Measurement Based on Embedded Accelerometer in a Smartphone

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