4-Stage ECG Segmentation (SEG-4-TCN-LG)

Overview

The following table provides the latest pre-trained model for 4-class ECG segmentation. Below we also provide additional details including training configuration, performance metrics, and downloads.

NAME	DATASET	FS	DURATION	# CLASSES	MODEL	PARAMS	FLOPS	METRIC
SEG-4-TCN-LG	LUDB, Synthetic	100Hz	2.5s	4	TCN	10K	3.9M	89.4% F1

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Input

The model is trained on 2.5-second, raw ECG frames sampled at 100 Hz.

• Sensor: ECG
• Location: Wrist
• Sampling Rate: 100 Hz
• Frame Size: 2.5 seconds

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Class Mapping

The model is able to segment ECG signals into four classes: P-wave, QRS complex, T-wave, and none. The class mapping is as follows:

Base Class	Target Class	Label
0-NONE	0	NONE
1-PWAVE	1	PWAVE
2-QRS	2	QRS
3-TWAVE	3	TWAVE

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Datasets

The model is trained on the following datasets:

• Lobachevsky University Electrocardiography dataset (LUDB)
• Synthetic

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Model Performance

The confusion matrix for the segmentation model is depicted below.

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Downloads

Asset	Description
configuration.json	Configuration file
model.keras	Keras Model file
model.tflite	TFLite Model file
metrics.json	Metrics file

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