Low Power Personalized ECG Based System Design Methodology for Remote Cardiac Health Monitoring

Abstract

This paper describes a mixed-signal electrocardiogram (ECG) system for personalized and remote cardiac health monitoring. The novelty of this paper is fourfold. First, a low power analog front end with an efficient automatic gain control mechanism, maintaining the input of the ADC to a level rendering optimum SNR and the enhanced recyclic folded cascode opamp used as an integrator for ADC. Second, a novel on-the-fly PQRST boundary detection (BD) methodology is formulated for finding the boundaries in continuous ECG signal. Third, a novel low-complexity ECG feature extraction architecture is designed by reusing the same module present in the proposed BD methodology. Fourth, the system is having the capability to reconfigure the proposed low power ADC for low (8 b) and high (12 b) resolution with the use of the feedback signal obtained from the digital block when it is in processing. The proposed system has been tested and validated on patient's data from PTBDB, CSEDB, and in-house IIT Hyderabad Data Base (IITHDB) and we have achieved an accuracy of 99% upon testing on various normal and abnormal ECG signals. The whole system is implemented in 180-nm technology resulting in 9.47-µW (at 1 MHz) power consumption and occupying 1.74-mm 2 silicon area.

Phase Space Reconstruction Based CVD Classifier Using Localized Features

“…10. The boundaries of each ECG beat from the continuous ECG wave of each array are extracted using our proposed Boundary Detection (BD) block 25 as shown in Fig. 11, making use of these start and end boundary indexes of each ECG beat we have extracted the localized features (PR Interval, QRS complex, and QT interval) using our proposed Feature Extraction (FE) block 25,26 .…”

“…The boundaries of each ECG beat from the continuous ECG wave of each array are extracted using our proposed Boundary Detection (BD) block 25 as shown in Fig. 11, making use of these start and end boundary indexes of each ECG beat we have extracted the localized features (PR Interval, QRS complex, and QT interval) using our proposed Feature Extraction (FE) block 25,26 . The proposed work is feature-based classification methodology where all the localized features (PR interval, QRS complex, and QT interval) are accessed or extracted using the method proposed in our earlier work 25 .…”

“…11, making use of these start and end boundary indexes of each ECG beat we have extracted the localized features (PR Interval, QRS complex, and QT interval) using our proposed Feature Extraction (FE) block 25,26 . The proposed work is feature-based classification methodology where all the localized features (PR interval, QRS complex, and QT interval) are accessed or extracted using the method proposed in our earlier work 25 . Authors have contributed in the domain of accurate online features extractions that had been evaluated against the widely accepted publically available database (PTBDB, MIT-DB, IAFDB, CSEDB).…”

Improving accuracy of derived 12-lead electrocardiography by waveform segmentation

“…Advanced technologies have been implemented to reduce this number and -at the same time -to increase life expectancy. The focuses is prevention [3], including prediction and early diagnosis [4], for instance personalized cardiovascular disease monitoring devices [5].…”

Two-dimensional arrays of vertically packed spin-valves with picoTesla sensitivity at room temperature

“…Despite the very low field detection capability, SQUIDs require cryogenic environments 13 and OPMs rely on complex optical parts 14 , hampering a seamless compact and wearable design. Therefore, the next generation of medical devices demands small and robust solutions with low power consumption and high sensitivity 15,16 , compatible with electronic integration 17,18 and flexible substrates 19 for enhanced portability and compactness. A reliable solution for compact devices working at a room temperature can be found in magnetoresistive (MR) sensors [20][21][22] .…”