Comparing Time and Frequency Domain Heart Rate Variability for Deep Learning-Based Glucose Detection

Shaqiri, Ervin; Gušev, Marjan; Poposka, Lidija; Vavlukis, Marija; Ahmeti, Irfan

doi:10.1007/978-3-031-04206-5_14

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ECG data, with its nuances, presents both spatial and temporal challenges. While LSTMs aptly address the temporal aspects, spatial feature extraction becomes a stumbling block [8]. This is where convolutional neural networks (CNN) come into the picture.…”

Section: Introductionmentioning

confidence: 99%

Deep Conv-LSTM Network for Arrhythmia Detection using ECG Data

Mukhametkaly,

Momynkulov,

Kurmanbekkyzy

et al. 2023

IJACSA

View full text Add to dashboard Cite

In the evolving realm of medical diagnostics, electrocardiogram (ECG) data stands as a cornerstone for cardiac health assessment. This research introduces a novel approach, leveraging the capabilities of a Deep Convolutional Long Short-Term Memory (Conv-LSTM) network for the early and accurate detection of arrhythmias using ECG data. Traditionally, cardiac anomalies have been diagnosed through heuristic means, often requiring intricate scrutiny and expertise. However, the Deep Conv-LSTM model proposed herein addresses the inherent limitations of traditional methods by amalgamating the spatial feature extraction capability of convolutional neural networks (CNN) with the temporal sequence learning capacity of LSTM networks. Initial results derived from a diverse dataset, comprising myriad ECG waveform anomalies, delineated an enhancement in accuracy, reducing false positives and facilitating timely interventions. Notably, the model showcased adaptability in handling the burstiness of ECG signals, reflecting various heart rhythms, and the perplexity inherent in diagnosing subtle arrhythmic events. Additionally, the model's ability to discern longer, more complex patterns alongside transient anomalies offers potential for broader applications in telemetry and continuous patient monitoring systems. It is anticipated that this innovative fusion of CNN and LSTM architectures will usher a paradigm shift in automated arrhythmia detection, bridging the chasm between technology and the intricate nuances of cardiac physiology, thus improving patient outcomes.

show abstract

Section: Introductionmentioning

confidence: 99%