Datasets:

abmallick
/

heart-breath-sleep-stage-dataset

time_seconds float64 0 110k	heart_rate float64 30 214	respiratory_rate float64 4 50	sleep_stage int64 0 9	hr_mean float64 30 214	hr_sdnn_5 float64 0 64.4	hr_rmssd_5 float64 0 72.9	hr_slope_3 float64 -58.97 65	rr_mean float64 4 50	rr_sd_5 float64 0 25.1	rr_slope_3 float64 -28.46 35.5	hr_rr_ratio float64 0.69 36.6	hr_rr_product float64 146 7.88k	minutes_since_start float64 0 1.84k	night_id int64 1 10.3k	split stringclasses 3 values
0	87.42	7.23	5	87.42	0.93	1.31	1.31	7.23	1.45	2.05	12.09	632.05	0	1	train
30	88.73	9.28	5	88.73	0.93	1.31	1.31	9.28	1.45	2.05	9.56	823.41	0.5	1	train
60	90.29	21.51	5	90.29	1.44	1.44	1.44	21.51	7.72	7.14	4.2	1,942.14	1	1	train
90	86.83	27.59	5	86.83	1.54	2.32	-0.2	27.59	9.77	6.79	3.15	2,395.64	1.5	1	train
120	85.86	27.07	5	85.86	1.73	2.07	-0.96	27.07	9.71	5.93	3.17	2,324.23	2	1	train
150	85.45	20.96	5	85.45	2.04	1.97	-1.61	20.96	7.37	-0.18	4.08	1,791.03	2.5	1	train
210	108.78	26.87	0	108.78	9.88	11.8	7.32	26.87	3.27	-0.24	4.05	2,922.92	3.5	1	train
240	94.74	21.09	0	94.74	9.95	13.62	2.96	21.09	3.38	-1.99	4.49	1,998.07	4	1	train
270	86.65	15.61	0	86.65	9.97	14.2	0.4	15.61	4.79	-1.78	5.55	1,352.61	4.5	1	train
300	81.67	15.61	0	81.67	10.79	14.42	-9.04	15.61	4.69	-3.75	5.23	1,274.87	5	1	train
330	81.65	15.97	0	81.65	11.44	8.48	-4.36	15.97	4.96	-1.71	5.11	1,303.95	5.5	1	train
390	84.38	14.33	0	84.38	5.41	4.94	-0.76	14.33	2.63	-0.43	5.89	1,209.17	6.5	1	train
420	82.33	14.34	0	82.33	2.16	3.02	0.22	14.34	0.78	-0.42	5.74	1,180.61	7	1	train
450	83.36	18.1	0	83.36	1.18	1.78	0.57	18.1	1.55	0.71	4.61	1,508.82	7.5	1	train
480	81.74	16.61	0	81.74	1.16	1.96	-0.88	16.61	1.6	0.76	4.92	1,357.7	8	1	train
510	80.86	14.29	0	80.86	1.38	1.47	-0.49	14.29	1.74	-0.02	5.66	1,155.49	8.5	1	train
540	81.96	18.05	0	81.96	0.91	1.19	-0.47	18.05	1.89	-0.02	4.54	1,479.38	9	1	train
570	78.99	13.48	0	78.99	1.61	1.83	-0.92	13.48	2.13	-1.04	5.86	1,064.79	9.5	1	train
600	81.08	16.6	1	81.08	1.17	1.95	0.07	16.6	1.87	0.77	4.88	1,345.93	10	1	train
660	80.24	22.12	1	80.24	1.1	1.94	-0.57	22.12	3.43	1.36	3.63	1,774.91	11	1	train
690	81.2	20.53	1	81.2	1.13	1.92	0.74	20.53	3.38	2.35	3.96	1,667.04	11.5	1	train
750	86.41	21.43	0	86.41	2.84	2.88	1.78	21.43	3.68	1.61	4.03	1,851.77	12.5	1	train
780	83.42	26.09	1	83.42	2.5	3.07	1.06	26.09	3.4	1.32	3.2	2,176.43	13	1	train
810	80.28	16.6	1	80.28	2.63	3.42	-0.31	16.6	3.4	-1.31	4.84	1,332.65	13.5	1	train
840	80.52	16	1	80.52	2.58	3.39	-1.96	16	4.09	-1.81	5.03	1,288.32	14	1	train
870	79.07	14.17	1	79.07	2.97	2.29	-1.45	14.17	4.85	-3.97	5.58	1,120.42	14.5	1	train
900	77.67	13.95	1	77.67	2.13	1.87	-0.87	13.95	5.01	-0.88	5.57	1,083.5	15	1	train
930	79.77	16.84	1	79.77	1.15	1.46	-0.25	16.84	1.36	0.28	4.74	1,343.33	15.5	1	train
960	78.97	13.95	1	78.97	1.06	1.51	-0.03	13.95	1.35	-0.07	5.66	1,101.63	16	1	train
990	83.42	35.29	2	83.42	2.17	2.59	1.92	35.29	9.28	7.11	2.36	2,943.89	16.5	1	train
1,020	81.41	6.9	2	81.41	2.23	2.69	0.55	6.9	10.66	-3.31	11.8	561.73	17	1	train
1,050	79.45	21.9	2	79.45	1.82	2.66	0.16	21.9	10.61	2.65	3.63	1,739.95	17.5	1	train
1,080	82	18.9	2	82	1.84	2.92	-0.47	18.9	10.55	-5.46	4.34	1,549.8	18	1	train
1,110	80.39	13.74	2	80.39	1.52	2.06	-0.34	13.74	10.57	2.28	5.85	1,104.56	18.5	1	train
1,140	80.09	17.91	2	80.09	1.03	1.8	0.21	17.91	5.8	-1.33	4.47	1,434.41	19	1	train
1,170	78.82	20.61	2	78.82	1.2	1.64	-1.06	20.61	3.13	0.57	3.82	1,624.48	19.5	1	train
1,200	79.2	18.75	2	79.2	1.24	1.05	-0.4	18.75	2.57	1.67	4.22	1,485	20	1	train
1,230	78.89	13.9	2	78.89	0.72	0.7	-0.4	13.9	3.05	-1.34	5.68	1,096.57	20.5	1	train
1,260	78.59	20.38	2	78.59	0.59	0.7	-0.08	20.38	2.71	-0.08	3.86	1,601.66	21	1	train
1,290	78.5	16.22	2	78.5	0.28	0.29	-0.23	16.22	2.87	-0.84	4.84	1,273.27	21.5	1	train
1,320	78.43	18.18	2	78.43	0.32	0.22	-0.15	18.18	2.5	1.43	4.31	1,425.86	22	1	train
1,350	78.78	13.24	2	78.78	0.19	0.24	0.06	13.24	2.97	-2.38	5.95	1,043.05	22.5	1	train
1,380	78.23	15.91	2	78.23	0.2	0.33	-0.09	15.91	2.67	-0.1	4.92	1,244.64	23	1	train
1,410	78.8	15.11	2	78.8	0.24	0.43	0.12	15.11	1.79	-1.02	5.22	1,190.67	23.5	1	train
1,440	76.61	20.3	2	76.61	0.9	1.18	-0.72	20.3	2.75	2.35	3.77	1,555.18	24	1	train
1,470	77.69	21.26	2	77.69	0.91	1.28	-0.18	21.26	3.46	1.78	3.65	1,651.69	24.5	1	train
1,500	77.69	18.68	2	77.69	0.81	1.25	-0.37	18.68	2.68	1.19	4.16	1,451.25	25	1	train
1,530	78	20.45	2	78	0.79	1.23	0.46	20.45	2.45	0.05	3.81	1,595.1	25.5	1	train
1,560	78.23	19.43	2	78.23	0.62	0.57	0.18	19.43	0.99	-0.61	4.03	1,520.01	26	1	train
1,590	78.8	14.06	2	78.8	0.46	0.34	0.37	14.06	2.81	-1.54	5.6	1,107.93	26.5	1	train
1,620	77.24	19.12	2	77.24	0.58	0.85	-0.25	19.12	2.48	-0.44	4.04	1,476.83	27	1	train
1,650	78.1	14.88	2	78.1	0.56	0.94	-0.04	14.88	2.9	-1.52	5.25	1,162.13	27.5	1	train
1,680	78.67	17.65	2	78.67	0.62	0.98	-0.04	17.65	2.45	1.2	4.46	1,388.53	28	1	train
1,710	77.63	16.47	2	77.63	0.67	1.07	0.13	16.47	2.05	-0.88	4.71	1,278.57	28.5	1	train
1,740	77.34	14.12	2	77.34	0.59	0.75	-0.25	14.12	2.03	-0.25	5.48	1,092.04	29	1	train
1,770	76.65	19.92	2	76.65	0.76	0.7	-0.67	19.92	2.3	0.76	3.85	1,526.87	29.5	1	train
1,800	76.94	19.78	2	76.94	0.78	0.66	-0.23	19.78	2.43	1.1	3.89	1,521.87	30	1	train
1,830	76.35	22.88	2	76.35	0.51	0.5	-0.33	22.88	3.39	2.92	3.34	1,746.89	30.5	1	train
1,860	76.67	16.16	2	76.67	0.37	0.5	0.01	16.16	3.45	-1.25	4.74	1,238.99	31	1	train
1,890	76.39	22.53	2	76.39	0.24	0.39	-0.18	22.53	2.7	0.92	3.39	1,721.07	31.5	1	train
1,920	75.52	16.94	2	75.52	0.53	0.57	-0.28	16.94	3.09	-1.98	4.46	1,279.31	32	1	train
1,950	76.31	21.01	2	76.31	0.43	0.62	-0.12	21.01	3.15	1.62	3.63	1,603.27	32.5	1	train
2,010	75.18	23.38	2	75.18	0.63	0.83	-0.4	23.38	3.28	0.28	3.22	1,757.71	33.5	1	train
2,040	75.74	30.72	2	75.74	0.52	0.86	0.07	30.72	5.02	4.59	2.47	2,326.73	34	1	train
2,070	74.52	22.73	2	74.52	0.66	0.96	-0.6	22.73	5.01	0.57	3.28	1,693.84	34.5	1	train
2,100	74.84	20.77	2	74.84	0.72	0.89	-0.11	20.77	4.07	-0.87	3.6	1,554.43	35	1	train
2,130	74.76	20.45	2	74.76	0.47	0.69	-0.33	20.45	4.17	-3.42	3.66	1,528.84	35.5	1	train
2,160	75.14	20.76	2	75.14	0.47	0.66	0.21	20.76	4.36	-0.66	3.62	1,559.91	36	1	train
2,190	73.49	26.18	2	73.49	0.63	0.86	-0.45	26.18	2.41	1.8	2.81	1,923.97	36.5	1	train
2,220	73.89	20.38	2	73.89	0.7	0.87	-0.29	20.38	2.51	-0.02	3.63	1,505.88	37	1	train
2,250	72.23	23.81	2	72.23	1.14	1.2	-0.97	23.81	2.59	1.02	3.03	1,719.8	37.5	1	train
2,280	73.19	15.91	2	73.19	1.06	1.28	-0.1	15.91	3.88	-3.42	4.6	1,164.45	38	1	train
2,310	72.8	21.9	2	72.8	0.64	1	-0.36	21.9	3.87	0.51	3.32	1,594.32	38.5	1	train
2,340	73.21	22.3	2	73.21	0.61	1	0.33	22.3	3.02	-0.5	3.28	1,632.58	39	1	train
2,370	73.22	22.22	2	73.22	0.43	0.56	0.01	22.22	3.06	2.1	3.3	1,626.95	39.5	1	train
2,400	72.33	22.64	2	72.33	0.39	0.53	-0.16	22.64	2.85	0.25	3.19	1,637.55	40	1	train
2,430	76.72	33.33	2	76.72	1.75	2.25	1.17	33.33	4.96	3.68	2.3	2,557.08	40.5	1	train
2,460	74.1	20.17	2	74.1	1.69	2.59	0.29	20.17	5.23	-0.68	3.67	1,494.6	41	1	train
2,490	71.9	15.79	2	71.9	1.91	2.82	-0.14	15.79	6.47	-2.28	4.55	1,135.3	41.5	1	train
2,520	71.37	16.03	2	71.37	2.18	2.8	-1.78	16.03	7.17	-5.77	4.45	1,144.06	42	1	train
2,550	71.94	21.9	2	71.94	2.23	1.75	-0.72	21.9	7.15	0.58	3.28	1,575.49	42.5	1	train
2,580	72.82	21.51	2	72.82	1.07	1.25	0.31	21.51	2.97	1.91	3.39	1,566.36	43	1	train
2,610	71.87	24.72	2	71.87	0.52	0.76	0.17	24.72	3.93	2.9	2.91	1,776.63	43.5	1	train
2,640	72.8	20.93	2	72.8	0.63	0.85	0.29	20.93	3.15	-0.32	3.48	1,523.7	44	1	train
2,670	71.94	19.05	2	71.94	0.49	0.91	-0.29	19.05	2.05	-0.82	3.78	1,370.46	44.5	1	train
2,700	72.37	16.41	2	72.37	0.45	0.82	0.17	16.41	3.08	-2.77	4.41	1,187.59	45	1	train
2,730	71.98	19	3	71.98	0.39	0.7	-0.27	19	3.08	-0.64	3.79	1,367.62	45.5	1	train
2,760	72.21	18.34	2	72.21	0.35	0.53	0.09	18.34	1.62	-0.24	3.94	1,324.33	46	1	train
2,790	72.42	17.14	2	72.42	0.22	0.33	0.02	17.14	1.17	0.24	4.23	1,241.28	46.5	1	train
2,820	72.44	15.58	2	72.44	0.19	0.25	0.15	15.58	1.39	-1.14	4.65	1,128.62	47	1	train
2,850	72.8	16.47	3	72.8	0.3	0.24	0.2	16.47	1.38	-0.62	4.42	1,199.02	47.5	1	train
2,880	71.35	13.95	3	71.35	0.54	0.75	-0.36	13.95	1.65	-1.06	5.11	995.33	48	1	train
2,910	71.98	19.05	3	71.98	0.56	0.81	-0.15	19.05	1.89	1.16	3.78	1,371.22	48.5	1	train
2,940	70.85	20.34	3	70.85	0.79	0.99	-0.65	20.34	2.59	1.29	3.48	1,441.09	49	1	train
2,970	71.26	16.54	3	71.26	0.76	0.99	-0.03	16.54	2.49	0.86	4.31	1,178.64	49.5	1	train
3,000	71.31	20.87	3	71.31	0.41	0.68	-0.22	20.87	2.88	0.61	3.42	1,488.24	50	1	train
3,030	71.07	25.9	3	71.07	0.42	0.61	0.07	25.9	3.43	1.85	2.74	1,840.71	50.5	1	train
3,060	71	20.38	3	71	0.19	0.24	-0.09	20.38	3.34	1.28	3.48	1,446.98	51	1	train
3,090	70.68	18.69	3	70.68	0.25	0.2	-0.21	18.69	3.47	-0.73	3.78	1,321.01	51.5	1	train
3,120	71.16	22.06	3	71.16	0.23	0.31	0.03	22.06	2.7	-1.28	3.23	1,569.79	52	1	train

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Heart‑Breath Sleep Stage Dataset

A public dataset for physiological sleep analysis that contains heart rate and respiratory rate signals alongside sleep stage labels, collected over overnight sleep sessions. Each row represents a short time window from a night recording with derived cardiorespiratory features and the corresponding sleep stage.

Dataset link: https://huggingface.co/datasets/abmallick/heart-breath-sleep-stage-dataset

📊 Dataset Overview

Name: Heart‑Breath Sleep Stage Dataset
Author: Abhinav Mallick
Modalities: Tabular / Time‑series (derived features)
Format: Parquet
License: MIT

🧠 Motivation

Sleep staging is traditionally performed using EEG‑based polysomnography, which is expensive, intrusive, and difficult to scale. This dataset focuses on heart rate (HR) and respiratory rate (RR) dynamics, which are known to correlate strongly with sleep stages, enabling:

Non‑EEG sleep staging research
Wearable and mattress‑based sleep monitoring
Low‑cost, scalable sleep health solutions

The dataset is suitable for both classical ML and deep learning approaches.

📋 Features / Columns

Each row corresponds to a time window within a sleep recording.

Column	Description
`time_seconds`	Time since start of recording (seconds)
`minutes_since_start`	Time since start (minutes)
`heart_rate`	Instantaneous heart rate (bpm)
`respiratory_rate`	Instantaneous respiratory rate
`hr_mean`	Mean heart rate for the window
`hr_sdnn_5`	SDNN HRV metric (5‑min window)
`hr_rmssd_5`	RMSSD HRV metric (5‑min window)
`hr_slope_3`	Heart‑rate trend (3‑min slope)
`rr_mean`	Mean respiratory rate
`rr_sd_5`	Respiratory rate standard deviation
`rr_slope_3`	Respiratory‑rate trend
`hr_rr_ratio`	HR to RR ratio
`hr_rr_product`	HR × RR derived feature
`sleep_stage`	Sleep stage label (integer‑encoded)
`classes`	Human‑readable sleep stage label
`night_id`	Unique identifier per night/session
`split`	Dataset split (`train` / `val` / `test`)

🧩 Sleep Stage Labels

The sleep_stage column is integer‑encoded. Depending on usage, labels can be mapped to:

Wake
REM
Light sleep (N1/N2)
Deep sleep (N3)

Users may collapse or remap classes based on their modeling strategy.

🚀 Quick Start

Installation

pip install datasets pandas pyarrow

Load Dataset

from datasets import load_dataset

dataset = load_dataset(
    "abmallick/heart-breath-sleep-stage-dataset",
    split="train"
)

print(dataset[0])

Convert to Pandas

df = dataset.to_pandas()
df.head()

📈 Example Use Cases

Sleep stage classification models
HRV and respiratory pattern analysis across sleep cycles
Time‑series modeling with RNNs, TCNs, or Transformers
Research on non‑EEG sleep monitoring systems
Feature engineering for wearable or edge‑AI pipelines

🧪 Suggested Evaluation Metrics

Accuracy
Macro / Weighted F1‑score
Cohen’s Kappa
Per‑class Recall

📚 Citation

If you use this dataset in your research or projects, please cite:

@misc{mallick2025heartbreathsleep,
  title={Heart‑Breath Sleep Stage Dataset},
  author={Mallick, Abhinav},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/abmallick/heart-breath-sleep-stage-dataset}
}

🤝 Contributions

Issues, improvements, and discussions are welcome via the Hugging Face dataset page.

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