Portable Real Time ECG Monitor and Disease Diagnostics

Hasan, Mahmudul; Rahman, Sajjadur; Arefeen, Asiful; Ahmed, Tanvir; Nakib, Mohtasim; Shahnaz, Celia; Subhana, Arik

doi:10.1109/becithcon48839.2019.9063172

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…For the last decade, there has been immense development in the domain of wearable devices for physiological parameter monitoring, disease diagnosis and early prevention. Sleep apnea monitoring [7,8], cardiac anomalies or arrhythmia detection/classification [9,10,11], HRV estimation [12], ECG monitoring [13], and ophthalmic disease diagnosis [14] are some of the tasks worth mentioning. Although PPG sensors embedded in various wearables such as smart watches and smart rings enable convenient and continuous measurement of IBI, extracting IBI values from PPG is challenging as PPG is highly prone to noise.…”

Section: Introductionmentioning

confidence: 99%

Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error

Arefeen,

Akbari,

Mirzadeh

et al. 2021

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Inter-beat interval (IBI) measurement enables estimation of heart-tare variability (HRV) which, in turns, can provide early indication of potential cardiovascular diseases. However, extracting IBIs from noisy signals is challenging since the morphology of the signal is distorted in the presence of the noise. Electrocardiogram (ECG) of a person in heavy motion is highly corrupted with noise, known as motion-artifact, and IBI extracted from it is inaccurate. As a part of remote health monitoring and wearable system development, denoising ECG signals and estimating IBIs correctly from them have become an emerging topic among signal-processing researchers. Apart from conventional methods, deep-learning techniques have been successfully used in signal denoising recently, and diagnosis process has become easier, leading to accuracy levels that were previously unachievable. We propose a deep-learning approach leveraging tiramisu autoencoder model to suppress motion-artifact noise and make the R-peaks of the ECG signal prominent even in the presence of high-intensity motion. After denoising, IBIs are estimated more accurately expediting diagnosis tasks. Results illustrate that our method enables IBI estimation from noisy ECG signals with SNR up to -30dB with average root mean square error (RMSE) of 13 milliseconds for estimated IBIs. At this noise level, our error percentage remains below 8% and outperforms other state of the art techniques.

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