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:
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 |