Signal Processing Architecture Implementation Methodologies for Next-Generation Remote Healthcare Systems

Acharyya, Amit

doi:10.1007/978-1-4614-8842-2_4

Cited by 1 publication

(1 citation statement)

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“…Rule Engine (RE), as seen in Fig. 1(b), decides the abnormality of the signal based on the extracted features [25], [26] and takes an intelligent decision dynamically on the trade-off between on-board processing or RF communication [9].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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Section: Introductionmentioning

confidence: 99%