Digital Filters for Real-Time ECG Signal Processing Using Microprocessors

1985 ◽  
Vol BME-32 (9) ◽  
pp. 708-713 ◽  
Author(s):  
M. L. Ahlstrom ◽  
W. J. Tompkins
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Digital Filters ◽  
Ecg Signal ◽  
Ecg Signal Processing

scholarly journals Efficient Real-Time R and QRS Detection Method Using a Pair of Derivative Filters and Max Filter for Portable ECG Device

2019 ◽  
Vol 9 (19) ◽  
pp. 4128
Author(s):  
Tae Wuk Bae ◽  
Kee Koo Kwon
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Detection Method ◽  
Sliding Window ◽  
Ecg Signal ◽  
Qrs Detection ◽  
Real Time Processing ◽  
Ecg Signals ◽  
Ecg Signal Processing ◽  
Processing Techniques

Recently, with the active development of wearable electrocardiogram (ECG) devices such as smart-bands or portable ECG devices, efficient ECG signal processing technology that can be applied in real-time has been actively studied. However, a wearable ECG device is exposed to various noise situations, thereby reducing the reliability of the detected R point or QRS interval. In addition, as early warning techniques in healthcare systems have been studied, real-time ECG signal processing techniques have become very important in wearable ECG devices. In this paper, we propose an efficient real-time R and QRS detection method using two kinds of first-order derivative filters and a max filter to analyze ECG signals measured from wearable ECG devices in real-time. The proposed method detects the R point and QRS interval in units of a sliding window for real-time processing and combines the detected R points in each sliding window. Also, the reliability of the detected R points and RR intervals is examined through noise region analysis using the histogram characteristic of a sample point. The performance of the proposed method was verified by the MIT-BIH database (DB), CYBHi DB and real ECG data measured from the developed wearable ECG patch. The proposed method achieves Se = 99.80%, +P = 99.80%, and DER = 0.36% against MIT-BIH DB. In addition, the proposed method enables accurate R point detection and heart rate variability (HRV) analysis even with noisy ECG signals.

Cloud-based real-time heart monitoring and ECG signal processing

2016 ◽  
Author(s):  
Fatima Bamarouf ◽  
Claire Crandell ◽  
Shannon Tsuyuki ◽  
Jose Sanchez ◽  
Yufeng Lu
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Ecg Signal ◽  
Heart Monitoring ◽  
Ecg Signal Processing

Real-Time Filtering and ECG Signal Processing Based on Dual-Core Digital Signal Controller System

2020 ◽  
Vol 20 (12) ◽  
pp. 6492-6503 ◽  
Author(s):  
Ngoc Thang Bui ◽  
Duc Tri Phan ◽  
Thanh Phuoc Nguyen ◽  
Giang Hoang ◽  
Jaeyeop Choi ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Digital Signal ◽  
Ecg Signal ◽  
Digital Signal Controller ◽  
Ecg Signal Processing ◽  
Dual Core
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