A
ECG
signal compression codec using Residual Vector Quantization (RVQ), optimized for edge and wearable devices.
Model Details
Modality:
ECG
Sample Rate:
256 Hz
Compression Ratio:
8x
Quantization:
INT8
RVQ Levels:
4
Codebook Size:
256 entries × 16D
Encoder Input:
[None, 1, 512, 1]
Encoder Output:
[None, 1, 64, 16]
Quality Metrics
Time Domain
Metric
Mean
Median
P90
PRD (%)
6.5681
5.7411
10.0261
RMSE
0.0631
0.0551
0.0956
Cosine Similarity
0.9973
0.9984
0.9992
Spectral
Band Total Relative Error (median):
0.0725
Bitrate
Codec CR (uniform):
8.0x
Codec CR (learned prior):
15.24x
Usage
Python (compressionkit runtime)
from compressionkit.runtime import RVQCodec
codec = RVQCodec.from_pretrained("Ambiq/compressionkit-ecg-8x")
# Encode: float32 signal → RVQ indices
indices = codec.encode(signal)
# Decode: RVQ indices → reconstructed signal
recon = codec.decode(indices)
Local deployment directory
codec = RVQCodec("path/to/deploy/")
Files
File
Description
encoder_int8.tflite
INT8 quantized encoder (on-device)
encoder.h
C header for encoder
decoder_float32.tflite
Float32 decoder (server-side evaluation)
decoder_int8.tflite
INT8 decoder (optional, on-device)
codebook.npz
RVQ codebook tables
codebook.h
C header for codebook
config.json
Deployment manifest
sample_stimulus.npz
Synthetic test data
quality_scorecard.json
Full evaluation metrics
Dataset & License
Training data: PTB-XL (CC BY 4.0). Sample data may include excerpts under the original license terms.
Model weights are released under the
Ambiq Model Weights License
— deployment is restricted to Ambiq silicon devices. See
LICENSE-MODEL-WEIGHTS.md
for full terms.
Citation
@software{compressionkit,
author = {Ambiq AI},
title = {compressionKIT: Signal Compression for Edge AI},
url = {https://github.com/AmbiqAI/compressionkit}
}
Runs of Ambiq compressionkit-ecg-8x on huggingface.co
5
Total runs
0
24-hour runs
1
3-day runs
-1
7-day runs
-41
30-day runs
More Information About compressionkit-ecg-8x huggingface.co Model
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