  Inter-beat interval (IBI) measurement enables estimation of heart-rate
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.
