| Name | pyskna JSON |
| Version |
0.1.0.post3
JSON |
| download |
| home_page | None |
| Summary | This toolbox provides computation methods for skin sympathetic nerve activity (SKNA), which can be derived from electrocardiogram (ECG) signals collected under specific recording conditions. |
| upload_time | 2025-08-05 23:26:13 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT |
| keywords |
skna
tvskna
iskna
biosignal
neuroscience
biomedical
mit
non-commercial
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# pyskna
This toolbox provides computation methods for skin nerve activity (SKNA), which can be derived from electrocardiogram (ECG) signals collected under specific recording conditions.
## Installation
```bash
pip install pyskna
```
## Example
```python
import pandas as pd
from pyskna import extract_iSKNA, extract_TVSKNA
# Load EKG file
EKG = pd.read_csv("example_ekg.csv", header=None).to_numpy().reshape(-1)
# iSKNA computation
SKNA_dict = extract_iSKNA(input_ary=EKG, fs=4000, f_l=500, f_h=1000, smoothing_win_len=0.1)
# TVSKNA computation
TVSKNA_dict = extract_TVSKNA(input_ary=EKG, fs=4000, smoothing_win_len=0.1, thread_n=12)
```
See [`test.py`](./tests/test.py)
## Compatibility for extract_TVSKNA
The extract_TVSKNA function uses VFCDM binaries, which has been tested with **Python 3.11** on the following platforms:
- macOS (Apple Silicon / ARM64)
- Linux (x86_64)
- Windows (x86_64)
### Notes
- macOS x86_64 (Intel) **is expected to work**, but has not been formally tested.
- 32-bit systems are **not supported**; the VFCDM library has only been compiled for 64-bit architectures.
## Citation
The neuECG technique for computing iSKNA has been implemented based on the following paper:
### iSKNA:
Kusayama, T., Wong, J., Liu, X., He, W., Doytchinova, A., Robinson, E. A., ... & Chen, P. S. (2020).
*Simultaneous noninvasive recording of electrocardiogram and skin sympathetic nerve activity (neuECG).*
**Nature Protocols**, 15(5), 1853–1877.
### TVSKNA:
If you use TVSKNA for 4kHz sampling frequency. Please cite the following paper:
- **For 4 kHz sampling:**
Kong, Y., Baghestani, F., D'Angelo, W., Chen, I. P., & Chon, K. H. (2025).
*A New Approach to Characterize Dynamics of ECG-Derived Skin Nerve Activity via Time-Varying Spectral Analysis.*
**IEEE Transactions on Affective Computing**.
- **For 500 and 1000 Hz sampling:**
Kong, Y., Baghestani, F., D'Angelo, W., Chen, I. P., & Chon, K. H. (2025).
*A New Approach to Characterize Dynamics of ECG-Derived Skin Nerve Activity via Time-Varying Spectral Analysis.*
**IEEE Transactions on Affective Computing**.
Kong, Y., Baghestani, F., Chen, I. P., & Chon, K. H. (2025).
*Feasibility of Extracting Skin Nerve Activity from Electrocardiogram Recorded at a Low Sampling Frequency.*
**arXiv preprint**, arXiv:2508.00494. https://doi.org/10.48550/arXiv.2508.00494
### ⚠️ Patent Notice
Some functions (e.g., **`process_tvskna`**) in this library interface with the **VFCDM** algorithm covered by
U.S. Patent No. **US8858450B2**, owned by Ki Chon, Ph.D. and Kihwan Ju, Ph.D.
(Assignee: Research Foundation of the State University of New York).
By using VFCDM‑related functions (e.g., **`process_tvskna`**) in PySKNA, you agree to the
[LICENSE.md](https://github.com/ykong-phd/vfcdm-binaries/blob/main/LICENSE.md) and [PATENT_NOTICE.md](https://github.com/ykong-phd/vfcdm-binaries/blob/main/PATENT_NOTICE.md) provided in the VFCDM Binaries repository, **solely for non-commercial, academic, and research use**.
Please see the PySKNA `LICENSE.md` for more details.
Commercial use of the patented components requires a separate license.
For patent‑related inquiries, contact **ki.chon@uconn.edu**.
For all other matters, contact **youngsun.kong.phd@gmail.com**.
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"description": "# pyskna\nThis toolbox provides computation methods for skin nerve activity (SKNA), which can be derived from electrocardiogram (ECG) signals collected under specific recording conditions.\n\n## Installation\n\n```bash\npip install pyskna\n```\n\n## Example\n\n\n\n```python\nimport pandas as pd\nfrom pyskna import extract_iSKNA, extract_TVSKNA\n\n# Load EKG file\nEKG = pd.read_csv(\"example_ekg.csv\", header=None).to_numpy().reshape(-1)\n\n# iSKNA computation\nSKNA_dict = extract_iSKNA(input_ary=EKG, fs=4000, f_l=500, f_h=1000, smoothing_win_len=0.1)\n\n# TVSKNA computation\nTVSKNA_dict = extract_TVSKNA(input_ary=EKG, fs=4000, smoothing_win_len=0.1, thread_n=12)\n\n```\nSee [`test.py`](./tests/test.py)\n\n## Compatibility for extract_TVSKNA\nThe extract_TVSKNA function uses VFCDM binaries, which has been tested with **Python 3.11** on the following platforms:\n- macOS (Apple Silicon / ARM64) \n- Linux (x86_64)\n- Windows (x86_64)\n\n### Notes\n- macOS x86_64 (Intel) **is expected to work**, but has not been formally tested.\n- 32-bit systems are **not supported**; the VFCDM library has only been compiled for 64-bit architectures.\n\n## Citation\nThe neuECG technique for computing iSKNA has been implemented based on the following paper:\n### iSKNA:\nKusayama, T., Wong, J., Liu, X., He, W., Doytchinova, A., Robinson, E. A., ... & Chen, P. S. (2020). \n*Simultaneous noninvasive recording of electrocardiogram and skin sympathetic nerve activity (neuECG).* \n**Nature Protocols**, 15(5), 1853\u20131877.\n\n### TVSKNA:\nIf you use TVSKNA for 4kHz sampling frequency. Please cite the following paper:\n\n- **For 4 kHz sampling:**\n\n Kong, Y., Baghestani, F., D'Angelo, W., Chen, I. P., & Chon, K. H. (2025). \n *A New Approach to Characterize Dynamics of ECG-Derived Skin Nerve Activity via Time-Varying Spectral Analysis.* \n **IEEE Transactions on Affective Computing**.\n\n- **For 500 and 1000 Hz sampling:**\n\n Kong, Y., Baghestani, F., D'Angelo, W., Chen, I. P., & Chon, K. H. (2025). \n *A New Approach to Characterize Dynamics of ECG-Derived Skin Nerve Activity via Time-Varying Spectral Analysis.* \n **IEEE Transactions on Affective Computing**.\n\n Kong, Y., Baghestani, F., Chen, I. P., & Chon, K. H. (2025). \n *Feasibility of Extracting Skin Nerve Activity from Electrocardiogram Recorded at a Low Sampling Frequency.* \n **arXiv preprint**, arXiv:2508.00494. https://doi.org/10.48550/arXiv.2508.00494\n\n\n\n\n### \u26a0\ufe0f Patent Notice\n\nSome functions (e.g., **`process_tvskna`**) in this library interface with the **VFCDM** algorithm covered by \nU.S. Patent No. **US8858450B2**, owned by Ki Chon, Ph.D. and Kihwan Ju, Ph.D. \n(Assignee: Research Foundation of the State University of New York).\n\nBy using VFCDM\u2011related functions (e.g., **`process_tvskna`**) in PySKNA, you agree to the \n[LICENSE.md](https://github.com/ykong-phd/vfcdm-binaries/blob/main/LICENSE.md) and [PATENT_NOTICE.md](https://github.com/ykong-phd/vfcdm-binaries/blob/main/PATENT_NOTICE.md) provided in the VFCDM Binaries repository, **solely for non-commercial, academic, and research use**.\n\n \n\nPlease see the PySKNA `LICENSE.md` for more details.\n\nCommercial use of the patented components requires a separate license. \n\nFor patent\u2011related inquiries, contact **ki.chon@uconn.edu**. \n\nFor all other matters, contact **youngsun.kong.phd@gmail.com**.\n",
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