2017 News

Slides presented at Computing in Cardiology in Rennes, France on the 26th-27th September 2017, and at the Annual Data Institute Conference at the USF Data Science Institute, San Francisco, CA, USA on the 17th October 2017 can be found here .

The dataset contains keystroke logs collected from over 200 subjects, with and without Parkinson's Disease (PD), as they typed normally on their own computer (without any supervision) over a period of weeks or months (having initially installed a custom keystroke recording app, Tappy). This dataset has been collected and analyzed in order to indicate that the routine interaction with computer keyboards can be used to detect changes in the characteristics of finger movement in the early stages of PD.

This database contains wrist PPGs recorded during walking, running and bike riding. Simultaneous motion estimates are collected using both accelerometers and gyroscopes to give multiple options for the removal of motion interference from the PPG traces. A reference chest ECG is included to allow a gold-standard comparison of heart rate during exercise.

A more detailed breakdown of performance of each official algorithm is now available here.

The official results are now available, as well as a summary paper of the Challenge.

The Pressure Map Dataset for In-bed Posture Classification database contains in-bed posture pressure data from multiple adult participants using two different types of pressure sensing mats.

Winners were announced at Computing in Cardiology 2017 in Rennes, France! Four teams were tied for first place, with an overall score of 0.83:

• Shreyasi Datta, Chetanya Puri, Ayan Mukherjee, Rohan Banerjee, Anirban Dutta Choudhury, Arijit Ukil, Soma Bandyopadhyay, Rituraj Singh, Arpan Pal, and Sundeep Khandelwal
• Shenda Hong, Meng Wu, Yuxi Zhou, Qingyun Wang, Junyuan Shang, Hongyan Li, and Junqing Xie
• Tomás Teijeiro, Constantino A. García, Paulo Félix, and Daniel Castro
• Morteza Zabihi, Ali Bahrami Rad, Aggelos K. Katsaggelos, Serkan Kiranyaz, Susanna Narkilahti, and Moncef Gabbouj

The Pattern Analysis of Oxygen Saturation Variability database contains one hour oxygen saturation measurements of 36 patients, used for the analysis of oxygen saturation variability.

The Evoked Auditory Responses in Hearing Impaired database contains evoked Auditory Brainstem Response (ABR) and Otoacoustic Emission (OAE) recordings in eight hearing impaired listeners, in response to tone-burst stimuli across a wide range of levels.

The database contains bioimpedance measurements, temperature measurements, salivary samples, and sweat samples, used in quantitative estimation of dehydration (total body water loss).

The MIMIC-III Waveform Database has been released, containing our largest collection to date of physiologic signals collected by bedside patient monitors in adult and neonatal intensive care units. Over 44,000 recordings have been added to the existing MIMIC-II Waveform Database, for a total of over 67,000 recordings and over three million hours of waveform data. For about one third of the database (about 22,000 recordings from 10,000 patients), extensive clinical information is also available to qualified researchers via the MIMIC-III Clinical Database.

The database contains non-EEG physiological signals collected at Quality of Life Laboratory at University of Texas at Dallas, used to infer the neurological status of 20 healthy subjects. The data collected consists of electrodermal activity, temperature, acceleration, heart rate, and arterial oxygen level.

The University of Cagliari electroTastegram database contains 39 differential biopotential measurements recorded from the tongues of as many healthy voluntary human subjects (16 males, 23 females, equally divided into the three PROP taster status classes), during a stimulation with 30uL, 3.2 mmol/L solution of 6-n-propylthiouracil (PROP).

The Shiraz University fetal heart sounds database contains fetal and maternal PCG recordings from 109 pregnant women.

The EEG Signals from an RSVP Task database contains EEG data from 11 healthy participants upon rapid presentation of images through the Rapid Serial Visual Presentation (RSVP) protocol at speeds of 5, 6, and 10 Hz.

The Tai Chi, Physiological Complexity, and Healthy Aging-Gait database contains two-channel gait recordings of 87 older adults from the Greater Boston area. Subjects were from a hybrid study design that included a two-arm randomized clinical trial (RCT) along with an additional observational comparison group.

The Preterm Infant Cardio-respiratory Signals (PICS) database contains simultaneous ECG and respiration recordings of ten preterm infants collected from the Neonatal Intensive Care Unit (NICU) of the University of Massachusetts Memorial Healthcare.

We are excited to announce the opening of the annual PhysioNet/Computing in Cardiology Challenge for 2017: AF Classification from a short single lead ECG recording. A database of over 10,000 ECG recordings is being made freely available exclusively for this competition by AliveCor, and represent a serendipitous sample of patient-initiated recordings of one minute or less. They have all been labeled for rhythm by hand into one of four categories: Normal, Atrial Fibrillation, Other Rhythm or Too Noisy to Process. We challenge the public to develop the most accurate classifier of these data into these four categories. As usual, a portion of these data have been hidden from the public to allow us to objectively assess your algorithms.

The STAFF-III database was acquired during 1995-96 at Charleston Area Medical Center (WV, USA) where single prolonged balloon inflation had been introduced to achieve optimal results of percutaneous transluminal coronary angiography (PTCA) procedures, replacing the typical series of brief inflations. The database consists of standard 12-lead ECG recordings from 104 patients.

The CHARIS database contains multi-channel recordings of ECG, arterial blood pressure (ABP), and intracranial pressure (ICP) of patients diagnosed with traumatic brain injury (TBI). The data is contributed by members of the CHARIS project which aims to systematize the analysis of relevant physiological signals, and create data-driven algorithms to search for potential predictors of acute clinical events for patients with acute brain injury.