An algorithm developed for inter and intra-patient paradigm that can be applied for detection of arrhythmias and atrial fibrillation using convolutional neural networks.

About

Description: Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmias. While there have been remarkable improvements in cardiac arrhythmia classification methods, they still cannot offer an acceptable performance in detecting different heart conditions, especially when dealing with imbalanced datasets. This technology addresses the limitations of current classification approaches by developing an automatic heartbeat classification method using deep convolutional neural networks and sequence-to-sequence models. Specifically, this method can be applied for detection of arrhythmias and atrial fibrillation (AF).

Potential applications: Clinical applications.

Benefits and advantages: This method significantly outperforms the existing algorithms in the literature for both intra-patient paradigm and inter-patient paradigm. Furthermore, this method can be applied on several other biomedical applications such as sleep staging where there are strong dependencies between each stage and sufficient data are not available. In addition, the present system can be used with wearable devices.

Case no.: 2019-020

Key Benefits

Benefits and advantages: This method significantly outperforms the existing algorithms in the literature for both intra-patient paradigm and inter-patient paradigm. Furthermore, this method can be applied on several other biomedical applications such as sleep staging where there are strong dependencies between each stage and sufficient data are not available. In addition, the present system can be used with wearable devices.

Applications

Potential applications: Clinical applications.

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