{"url":"/dataset/mths","name":"MTHS","full_name":null,"description_markdown":"the MTHS dataset contains 30Hz PPG signals obtained from\r\n62 patients, including 35 men and 27 women. The ground truth\r\ndata includes heart rate and oxygen saturation levels sampled\r\nat 1Hz. The HR and SPo2 measurement is obtained using a pulse oximeter (M70). An iPhone 5s was used to obtain the\r\nppg recordings at 30 fps.","description_withheld":null,"homepage":"https://github.com/MahdiFarvardin/MTVital","introduced_date":"2022-04-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/efficient-deep-learning-based-estimation-of","title":"Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones","first_author":"Taha Samavati","url":null},"license":{"name":"CC BY-NC-ND","url":"https://creativecommons.org/licenses/by-nc-nd/4.0/"},"modalities":[{"name":"Biomedical","url":"/datasets/modality/biomedical"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Photoplethysmography (PPG)","url":"/task/photoplethysmography-ppg","datasets_with_task":"/datasets/task/photoplethysmography-ppg"},{"name":"Heart rate estimation","url":"/task/heart-rate-estimation","datasets_with_task":"/datasets/task/heart-rate-estimation"},{"name":"SpO2 estimation","url":"/task/spo2-estimation","datasets_with_task":"/datasets/task/spo2-estimation"}],"languages":[],"variants":["MTHS"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/heart-rate-estimation-on-mths","task":"Heart rate estimation","dataset_variant":"MTHS","rows":1,"metrics":["MAE [bpm, session-wise]"],"first_row_in_archive_order":{"model":"Residual FCN","paper":"/paper/efficient-deep-learning-based-estimation-of","metrics":{"MAE [bpm, session-wise]":"6.96"},"code_links":[{"title":"mahdifarvardin/medvse","url":"https://github.com/mahdifarvardin/medvse"},{"title":"mahdifarvardin/mtvital","url":"https://github.com/mahdifarvardin/mtvital"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/spo2-estimation-on-mths","task":"SpO2 estimation","dataset_variant":"MTHS","rows":1,"metrics":["MAE [bpm, session-wise]"],"first_row_in_archive_order":{"model":"Residual FCN","paper":"/paper/efficient-deep-learning-based-estimation-of","metrics":{"MAE [bpm, session-wise]":"1.34"},"code_links":[{"title":"mahdifarvardin/medvse","url":"https://github.com/mahdifarvardin/medvse"},{"title":"mahdifarvardin/mtvital","url":"https://github.com/mahdifarvardin/mtvital"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/efficient-deep-learning-based-estimation-of","title":"Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones","date":"2022-04-13","rows_on_this_dataset":2,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}