Ambiq/compressionkit-ecg-32x-v1.0

compressionkit-ecg-32x

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: 32.0x
• Quantization: INT8
• RVQ Levels: 2
• Codebook Size: 256 entries × 16D
• Encoder Input: [None, 1, 512, 1]
• Encoder Output: [None, 1, 32, 16]

Quality Metrics

Fidelity & Robustness

Both fidelity yardsticks are reported so the codec is judged fairly: faithfulness is PRD vs the recorded (still-noisy) input, while truth fidelity is PRD vs clean ground truth. Lower is better.

Metric	Value
Truth PRD vs clean (%)	13.27
Truth PRD at native noise (%)	39.79
Faithful PRD vs input (%)	14.59
PRD degradation slope (PRD%/dB)	3.74
PRD at 0 dB SNR (%)	50.70
PRD at -6 dB SNR (%)	78.24
Pure-noise imprint autocorr	0.2682

Time Domain

PRD here is faithfulness (vs the recorded input); see Fidelity & Robustness above for the clean-truth and noise-regime view.

Metric	Mean	Median	P90
PRD vs input — faithfulness (%)	14.5856	12.9769	22.5875
RMSE	0.1400	0.1253	0.2141
Cosine Similarity	0.9872	0.9918	0.9962

Spectral

• Band Total Relative Error (median): 0.1852

Bitrate

• Codec CR (uniform): 32.0x
• Codec CR (learned prior): 59.87x

Usage

Python (compressionkit runtime)

from compressionkit.runtime import RVQCodec

codec = RVQCodec.from_pretrained("Ambiq/compressionkit-ecg-32x")

# 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}
}