Method and system of spiking neural network-based ecg classifier for wearable edge devices

A system of spiking neural network based Electrocardiogram (ECG) classifier for wearable edge devices comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces , wherein the one or more hardware processors are configured by the instructions to:
• obtaining by a spiking neural network a training dataset comprising a plurality of ECG time-series data from a plurality of sensors, wherein the spiking neural network comprise at least one of a first spiking neural network and a second spiking neural network;
• training at least one of the first spiking neural network and the second spiking neural network with the plurality of ECG time-series data to obtain a logistic regression-based ECG classifier, wherein the training comprises:
• feeding the plurality of ECG time-series data into a peak-based spike encoder of at least one of the spiking neural network to obtain a plurality of encoded spike trains from each spiking neural network based on one or more peaks detected from each ECG time-series data;
• providing the plurality of encoded spike trains into at least one of the spiking neural network, wherein a reservoir of the first spiking neural network obtains neuronal trace values of a plurality of recurrently connected excitatory neurons, wherein a spiking neural network layer (SNN) of the second spiking neural network obtains neuronal trace values of a plurality of neurons connected in a feed forward pattern;
• extracting from each ECG time-series data, a first set of spatio-temporal features are extracted from the first spiking neural network based on the neuronal trace values of the plurality of excitatory neurons, and a second set of spatio-temporal features are extracted from the second spiking neural network based on the neuronal trace values of the plurality of neurons connected in the feed forward pattern;
• training the logistic regression-based ECG classifier with one or more class labels associated with each of the spiking neural network by passing the neuronal trace values of the plurality of recurrently connected excitatory neurons into the first neural network and the neuronal trace values of the plurality of neurons connected in the feed forward pattern into the second neural network;
• and
• processing a plurality of test dataset received as input into at least one of the spiking neural network to obtain associated class label from the logistic regression-based ECG classifier.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• obtaining by a spiking neural network a training dataset comprising a plurality of ECG time-series data from a plurality of sensors, wherein the spiking neural network comprise at least one of a first spiking neural network and a second spiking neural network;
• training at least one of the first spiking neural network and the second spiking neural network with the plurality of ECG time-series data to obtain a logistic regression-based ECG classifier, wherein the training comprises:
• feeding the plurality of ECG time-series data into a peak-based spike encoder of at least one of the spiking neural network to obtain a plurality of encoded spike trains from each spiking neural network based on one or more peaks detected from each ECG time-series data;
• providing the plurality of encoded spike trains into at least one of the spiking neural network, wherein a reservoir of the first spiking neural network obtains neuronal trace values of a plurality of recurrently connected excitatory neurons, and wherein a spiking neural network (SNN) layer of the second spiking neural network obtains neuronal trace values of a plurality of neurons connected in a feed forward pattern;
• extracting from each ECG time-series data, a first set of spatio-temporal features from the first spiking neural network based on the neuronal trace values of the plurality of excitatory neurons, and a second set of spatio-temporal features from the second spiking neural network based on the neuronal trace values of the plurality of neurons connected in the feed forward pattern;
• training the logistic regression-based ECG classifier with one or more class labels associated with each of the spiking neural network by passing corresponding neuronal trace values of the plurality of recurrently connected excitatory neurons into the first neural network and the neuronal trace values of the plurality of neurons connected in the feed forward pattern into the second neural network;
• and
• processing a plurality of test dataset received as input into at least one of the spiking neural network to obtain associated class label from the logistic regression-based ECG classifier.