2-Stage ECG Segmentation (SEG-2-TCN-SM)

Overview

The following table provides the latest pre-trained model for 2-stage 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-2-TCN-SM	LUDB, Synthetic	100Hz	2.5s	2	TCN	2K	0.42M	96.6% F1

---

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

---

Class Mapping

The model is able to segment ECG signals into two classes: QRS complexes and none. The class mapping is as follows:

Base Class	Target Class	Label
0-NONE	0	NONE
2-QRS	1	QRS

---

Datasets

The model is trained on the following datasets:

• Lobachevsky University Electrocardiography dataset (LUDB)
• Synthetic

---

Model Performance

The confusion matrix for the model is depicted below.

---

Downloads

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

---