Advanced ECG processor with HRV analysis for real-time portable health monitoring

Chou, Chia-Ching; Tseng, Shao-Yen; Chua, Ericson; Lee, Yaw-Chern; Fang, Wai-Chi; Huang, Hsiang-Cheh

doi:10.1109/icce-berlin.2011.6031850

Cited by 23 publications

(5 citation statements)

References 7 publications

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“…With these On the one hand, with the progress of the signal processing techniques, more and more information can be derived from biosignals [19][20][21][22][23]. It has been widely demonstrated how capturing EMG and ECG signals of a person can efficiently and easily assess its health status [24,25], body posture [26], fitness level [27], and physical performance [28][29][30]. Besides, due to the fact that acquiring EMG and ECG signals need intrinsically noninvasive measurement devices, and are relatively easy to obtain, many applications based on them have been developed.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Channel Electromyography, Electrocardiography and Inertial Wireless Sensor Module Using Bluetooth Low-Energy

Biagetti¹,

Crippa²,

Falaschetti³

et al. 2020

Electronics

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This paper proposes a wireless sensor device for the real-time acquisition of bioelectrical signals such as electromyography (EMG) and electrocardiography (ECG), coupled with an inertial sensor, to provide a comprehensive stream of data suitable for human activity detection, motion analysis, and technology-assisted nursing of persons with physical or cognitive impairments. The sensor is able to acquire up to three independent bioelectrical channels (six electrodes), each with 24 bits of resolution and a sampling rate up to 3.2 kHz, and has a 6-DoF inertial platform measuring linear acceleration and angular velocity. The bluetooth low-energy wireless link was chosen because it allows easy interfacing with many consumer electronics devices, such as smartphones or tablets, that can work as data aggregators, but also imposes data rate restrictions. These restrictions are investigated in this paper as well, together with the strategy we adopted to maximize the available bandwidth and reliability of the transmission within the limits imposed by the protocol.

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Section: Introductionmentioning

confidence: 99%

A Multi-Channel Electromyography, Electrocardiography and Inertial Wireless Sensor Module Using Bluetooth Low-Energy

Biagetti¹,

Crippa²,

Falaschetti³

et al. 2020

Electronics

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“…The waveforms thus recorded have been standardized in terms of phase and amplitude relationships and any deviation from this would reflect or denote an abnormality in the functioning of the heart. Therefore understanding the electrical activity and the mechanical sequences performed by the heart in providing the driving force for the circulation of blood is very important [6]. A group of cells, located at the top right atrium, known as Sino-Auricular Node (SA node) is responsible for triggering the electrical activity of the heart.…”

Section: Introductionmentioning

confidence: 99%

Auto analysis of ECG signals using artificial neural network

Raj¹,

Dheetsith²,

Nair³

et al. 2014

2014 International Conference on Science Engineering and Management Research (ICSEMR)

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The greatest challenge faced during the process of diagnosis of cardiovascular diseases is the accurate analyses of the Electrocardiogram (ECG). Many researches are being done to classify and analyze the ECG signals automatically. In this paper, a novel method for the Auto analysis of the ECG signals using MATLAB is proposed and implemented. In this method, the raw ECG data obtained from the patient goes through a process of Wavelet Packet Decomposition (WPD) followed by Feature extraction. The classification is further done using Artificial Neural Network (ANN). This method, succeeding in differentiating the Abnormal ECG signals from the Normal signals, is proved to be a novel method for Auto analysis of ECG signals.

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“…It has been reported that heart rate variability analysis (HRVA) can recognize fatigue and stress conditions; it also can be utilized to detect drowsiness and diseases arising from fatigue and stress conditions [4,5]. To measure the heartbeat interval, the R-wave interval (RRI) obtained from an electrocardiograph (ECG) is mainly used [6,7]. Prior studies report that the pulse to pulse interval (PPI) obtained using photoplethysmography (PPG) can also be used for HRVA [8][9][10][11].…”

mentioning

confidence: 99%

Low-Noise Photoplethysmography Sensor Using Correlated Double Sampling for Heartbeat Interval Acquisition

Watanabe

Izumi

Sasai

et al. 2019

IEEE Trans. Biomed. Circuits Syst.

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Advanced ECG processor with HRV analysis for real-time portable health monitoring

Cited by 23 publications

References 7 publications

A Multi-Channel Electromyography, Electrocardiography and Inertial Wireless Sensor Module Using Bluetooth Low-Energy

A Multi-Channel Electromyography, Electrocardiography and Inertial Wireless Sensor Module Using Bluetooth Low-Energy

Auto analysis of ECG signals using artificial neural network

Low-Noise Photoplethysmography Sensor Using Correlated Double Sampling for Heartbeat Interval Acquisition

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