Journal ArticleDOI
Low-Power ECG-Based Processor for Predicting Ventricular Arrhythmia
Nourhan Bayasi,Temesghen Tekeste,Hani Saleh,Baker Mohammad,Ahsan H. Khandoker,Mohammed Ismail +5 more
Reads0
Chats0
TLDR
This paper presents the design of a fully integrated electrocardiogram (ECG) signal processor (ESP) for the prediction of ventricular arrhythmia using a unique set of ECG features and a naive Bayes classifier.Abstract:
This paper presents the design of a fully integrated electrocardiogram (ECG) signal processor (ESP) for the prediction of ventricular arrhythmia using a unique set of ECG features and a naive Bayes classifier. Real-time and adaptive techniques for the detection and the delineation of the P-QRS-T waves were investigated to extract the fiducial points. Those techniques are robust to any variations in the ECG signal with high sensitivity and precision. Two databases of the heart signal recordings from the MIT PhysioNet and the American Heart Association were used as a validation set to evaluate the performance of the processor. Based on application-specified integrated circuit (ASIC) simulation results, the overall classification accuracy was found to be 86% on the out-of-sample validation data with 3-s window size. The architecture of the proposed ESP was implemented using 65-nm CMOS process. It occupied 0.112- ${\rm mm}^{2}$ area and consumed 2.78- $\mu \text{W}$ power at an operating frequency of 10 kHz and from an operating voltage of 1 V. It is worth mentioning that the proposed ESP is the first ASIC implementation of an ECG-based processor that is used for the prediction of ventricular arrhythmia up to 3 h before the onset.read more
Citations
More filters
Journal ArticleDOI
Arrhythmia Recognition and Classification Using ECG Morphology and Segment Feature Analysis
TL;DR: A novel method is proposed for accurate recognition and classification of cardiac arrhythmia appearing with the presence of abnormal heart electrical activity and has a better performance by combining proposed features than by using the ECG morphology or ECG segment features separately.
Journal ArticleDOI
Human Vital Signs Detection Methods and Potential Using Radars: A Review.
Mamady Kebe,Rida Gadhafi,Rida Gadhafi,Baker Mohammad,Mihai Sanduleanu,Hani Saleh,Mahmoud Al-Qutayri +6 more
TL;DR: A thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques and a proof-of-concept of a radar- based vital sign detection system are presented.
Journal ArticleDOI
A Modular Low-Complexity ECG Delineation Algorithm for Real-Time Embedded Systems
TL;DR: This work presents a new modular and low-complexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets, and end), intended to perform real-time delineation on resource-constrained embedded systems.
Journal ArticleDOI
Ultra-Low Power QRS Detection and ECG Compression Architecture for IoT Healthcare Devices
TL;DR: A novel real-time QRS detector and an ECG compression architecture for IoT healthcare devices is presented that effectively enhances the QRS complex detection with minimized hardware resources and a lossless compression technique was incorporated into the proposed architecture.
Journal ArticleDOI
A 13.34 μ W Event-Driven Patient-Specific ANN Cardiac Arrhythmia Classifier for Wearable ECG Sensors
TL;DR: The proposed CTDA ANN-CAC can classify an arrhythmia within 252 μs at 25 MHz clock frequency with average power of 13.34 μW for 75bpm heart rate and shows over 98% classification accuracy, 97% sensitivity, and 94% positive predictivity on MIT-BIH database.
References
More filters
Journal ArticleDOI
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Journal ArticleDOI
A Real-Time QRS Detection Algorithm
Jiapu Pan,Willis J. Tompkins +1 more
TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Journal ArticleDOI
Sudden Cardiac Death
TL;DR: Total mortality, rather than classifications of cardiac and arrhythmic mortality, should be used as primary objectives for many outcome studies.
Journal ArticleDOI
Automatic classification of heartbeats using ECG morphology and heartbeat interval features
TL;DR: A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats and results are an improvement on previously reported results for automated heartbeat classification systems.
Journal ArticleDOI
Receiver operating characteristic (ROC) curve for medical researchers
Rajeev Kumar,Abhaya Indrayan +1 more
TL;DR: The aim of this article is to provide basic conceptual framework and interpretation of ROC analysis to help medical researchers to use it effectively.