Healthcare techniques through deep learning: issues, challenges and opportunities
In artificial intelligence, deep learning (DL) is a process that replicates the working
mechanism of the human brain in data processing, and it also creates patterns for decision …
mechanism of the human brain in data processing, and it also creates patterns for decision …
[HTML][HTML] Deep learning on 1-D biosignals: a taxonomy-based survey
N Ganapathy, R Swaminathan… - Yearbook of medical …, 2018 - thieme-connect.com
Objectives: Deep learning models such as convolutional neural networks (CNNs) have been
applied successfully to medical imaging, but biomedical signal analysis has yet to fully …
applied successfully to medical imaging, but biomedical signal analysis has yet to fully …
LSTM-based ECG classification for continuous monitoring on personal wearable devices
S Saadatnejad, M Oveisi… - IEEE journal of biomedical …, 2019 - ieeexplore.ieee.org
Objective: A novel electrocardiogram (ECG) classification algorithm is proposed for
continuous cardiac monitoring on wearable devices with limited processing capacity …
continuous cardiac monitoring on wearable devices with limited processing capacity …
A multi-context CNN ensemble for small lesion detection
In this paper, we propose a novel method for the detection of small lesions in digital medical
images. Our approach is based on a multi-context ensemble of convolutional neural …
images. Our approach is based on a multi-context ensemble of convolutional neural …
Anomaly detection in quasi-periodic time series based on automatic data segmentation and attentional LSTM-CNN
Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect
the anomalies of QTS. In this paper, we propose an a utomatic Q TS a nomaly d etection f …
the anomalies of QTS. In this paper, we propose an a utomatic Q TS a nomaly d etection f …
Differential diagnosis of Parkinson and essential tremor with convolutional LSTM networks
This study aims to present a novel method for differentiation of Parkinsonian tremor (PT) and
essential tremor (ET) using both postural and resting tremor. A convolutional long short-term …
essential tremor (ET) using both postural and resting tremor. A convolutional long short-term …
Deep Learning‐Based Data Augmentation and Model Fusion for Automatic Arrhythmia Identification and Classification Algorithms
S Ma, J Cui, W Xiao, L Liu - Computational Intelligence and …, 2022 - Wiley Online Library
Automated ECG‐based arrhythmia detection is critical for early cardiac disease prevention
and diagnosis. Recently, deep learning algorithms have been widely applied for arrhythmia …
and diagnosis. Recently, deep learning algorithms have been widely applied for arrhythmia …
Heart disease classification through crow intelligence optimization-based deep learning approach
Deep learning (DL) approaches provide predictive analysis capabilities for heart-related
diseases (HRD), enabling early pattern detection and identification of associated risk factors …
diseases (HRD), enabling early pattern detection and identification of associated risk factors …
Machine learning-data mining integrated approach for premature ventricular contraction prediction
Cardiac arrhythmias impose a significant burden on the healthcare environment due to the
increasing ratio of mortality worldwide. Arrhythmia and abnormal ECG heartbeat are the …
increasing ratio of mortality worldwide. Arrhythmia and abnormal ECG heartbeat are the …
An improved cardiac arrhythmia classification using stationary wavelet transform decomposed short duration QRS segment and Bi-LSTM network
Arrhythmia is a kind of cardiac conduction disorder those result in irregular heartbeats. The
electrocardiograph (ECG) signal may identify conduction system abnormalities. However, its …
electrocardiograph (ECG) signal may identify conduction system abnormalities. However, its …