Classification of ECG signals based on 1D convolution neural network

Li, Dan; Zhang, Jianxin; Zhang, Qiang; Wei, Xiaopeng

doi:10.1109/healthcom.2017.8210784

Cited by 155 publications

(86 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have shown that 1D Convolutional Neural Networks (CNNs) are well-suited to this task and vastly outperform other machine learning approaches, such as Support Vector Machines (SVMs) and simpler non-convolutional neural network architectures. It is well known that 1D CNNs work well when applied to pattern recognition problems involving timeseries signals such as Electrocardiography data [17], [32], particularly where the features of interest can occur at any point in time in a given signal. By contrast, conventional distance metrics and machine learning techniques do not perform well when the position of the target signal is highly variable, as confirmed by the results presented here.…”

Section: Discussionmentioning

confidence: 99%

1D Convolutional Neural Networks for Detecting Nystagmus

Newman

Phillips

Cox

2021

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

Vertigo is a type of dizziness characterised by the subjective feeling of movement despite being stationary. One in four individuals in the community experience symptoms of dizziness at any given time, and it can be challenging for clinicians to diagnose the underlying cause. When dizziness is the result of a malfunction in the innerear, the eyes flicker and this is called nystagmus. In this article we describe the first use of Deep Neural Network Architectures applied to detecting nystagmus. The data used in these experiments was gathered during a clinical investigation of a novel medical device for recording head and eye movements. We describe methods for training networks using very limited amounts of training data, with an average of 11 mins of nystagmus across four subjects, and less than 24 hours of data in total, per subject. Our methods work by replicating and modifying existing samples to generate new data. In a cross-fold validation experiment, we achieve an average F1 score of 0.59 (SD = 0.24) across all four folds, showing that the methods employed are capable of identifying periods of nystagmus with a modest degree of accuracy. Notably, we were also able to identify periods of pathological nystagmus produced by a patient during an acute attack of Ménière's Disease, despite training the network on nystagmus that was induced by different means.

show abstract

Section: Discussionmentioning

confidence: 99%

1D Convolutional Neural Networks for Detecting Nystagmus

Newman

Phillips

Cox

2021

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…For the model architecture in [14], it has three 1D CNN layers and two fully connected layers, exhibiting about 96.6% accuracy. In [17], only two 1D convolutional layers are used with two fully connected layers, demonstrating about 97.5% accuracy. We first implement these original non-split model from those two studies and then implement them using split learning to validate consistency in the model accuracy.…”

Section: D Cnn For Ecg Signal Classificationmentioning

confidence: 99%

“…We explain the detailed preprocessing steps in Appendix A. [17] model architecture we adopted is illustrated in Fig. 3 (a).…”

Section: D Cnn For Ecg Signal Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?

Abuadbba

Kim

et al. 2020

Proceedings of the 15th ACM Asia Conference on Computer and Communications Security

View full text Add to dashboard Cite

A new collaborative learning, called split learning, was recently introduced, aiming to protect user data privacy without revealing raw input data to a server. It collaboratively runs a deep neural network model where the model is split into two parts, one for the client and the other for the server. Therefore, the server has no direct access to raw data processed at the client. Until now, the split learning is believed to be a promising approach to protect the client's raw data; for example, the client's data was protected in healthcare image applications using 2D convolutional neural network (CNN) models. However, it is still unclear whether the split learning can be applied to other deep learning models, in particular, 1D CNN. In this paper, we examine whether split learning can be used to perform privacy-preserving training for 1D CNN models. To answer this, we first design and implement an 1D CNN model under split learning and validate its efficacy in detecting heart abnormalities using medical ECG data. We observed that the 1D CNN model under split learning can achieve the same accuracy of 98.9% like the original (non-split) model. However, our evaluation demonstrates that split learning may fail to protect the raw data privacy on 1D CNN models. To address the observed privacy leakage in split learning, we adopt two privacy leakage mitigation techniques: 1) adding more hidden layers to the client side and 2) applying differential privacy. Although those mitigation techniques are helpful in reducing

show abstract

“…In addition to RNNs, feedforward networks such as Convolutional Neural Networks (CNNs) [17,20] can be used for hand gesture recognition. The One-Dimensional (1D) CNNs have been found to be effective for a number of applications such as the classifications of ECG signals [21], human activities [22], and internet traffic [23]. Gesture recognition based on basic 1D CNNs may achieve high classification accuracy when the kernel sizes and/or the depth of the network are large.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes

Chu

Jhang²,

Tai³

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

The objective of this study is to present novel neural network (NN) algorithms and systems for sensor-based hand gesture recognition. The algorithms are able to classify accurately a sequence of hand gestures from the sensory data produced by accelerometers and gyroscopes. They are the extensions from the PairNet, which is a Convolutional Neural Network (CNN) capable of carrying out simple pairing operations with low computational complexities. Three different types of feedforward NNs, termed Residual PairNet, PairNet with Inception, and Residual PairNet with Inception are proposed for the extension. They are the PairNet operating in conjunction with short-cut connections and/or inception modules for achieving high classification accuracy and low computation complexity. A prototype system based on smart phones for remote control of home appliances has been implemented for the performance evaluation. Experimental results reveal that the PairNet has superior classification accuracy over its basic CNN and Recurrent NN (RNN) counterparts. Furthermore, the Residual PairNet, PairNet with Inception, and Residual PairNet with Inception are able to further improve classification hit rate and/or reduce recognition time for hand gesture recognition.

show abstract

Classification of ECG signals based on 1D convolution neural network

Cited by 155 publications

References 16 publications

1D Convolutional Neural Networks for Detecting Nystagmus

1D Convolutional Neural Networks for Detecting Nystagmus

Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?

Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes

Contact Info

Product

Resources

About