The MIT-BIH Arrhythmia Database contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Twenty-three recordings were chosen at random from a set of 4000 24-hour ambulatory ECG recordings collected from a mixed population of inpatients (about 60%) and outpatients (about 40%) at Boston's Beth Israel Hospital; the remaining 25 recordings were selected from the same set to include less common but clinically significant arrhythmias that would not be well-represented in a small random sample.

Interview protocols, recordings, and transcripts of three focus groups to investigate the social perception of AI and deepfake technology at the Massachusetts Institute of Technology. The focus groups are described below:

Focus Group #1 (engaged public): 12 participants in a 3-session Make A Fake class; the students were offered a full course refund in return for their participation in the study, which took place immediately following the final session of the class on Monday 27 February, 2023.

Focus Group #2 (attentive public) 14 visitors to the MIT Museum who volunteered to participate in the discussion after being recruited in the museum itself. The activity was scheduled for the week following recruitment, Monday 24 April, 2023, and as compensation for their involvement participants were offered a refund of their museum admission fee, and two more tickets for another day.

Focus Group #3 (nonattentive public): 13 pedestrians who were recruited with the help of 4 MIT volunteers working in the immediate environs of the Boston Public Library and the adjacent Prudential Center Shopping Mall. Participants were offered a $70 Amazon Gift Card in consideration for one hour of conversation on the same day of their recruitment, Saturday 27 May, 2023.

NOTE: Recordings from different devices are attached to better capture the voices of each conversation (devices: MacBook Air and iPad Pro).

Since 1975, our laboratories at Boston s Beth Israel Hospital (now the Beth Israel Deaconess Medical Center) and at MIT have supported our own research into arrhythmia analysis and related subjects. One of the first major products of that effort was the MIT-BIH Arrhythmia Database, which we completed and began distributing in 1980. The database was the first generally available set of standard test material for evaluation of arrhythmia detectors, and has been used for that purpose as well as for basic research into cardiac dynamics at more than 500 sites worldwide. Originally, we distributed the database on 9-track half-inch digital tape at 800 and 1600 bpi, and on quarter-inch IRIG-format FM analog tape. In August, 1989, we produced a CD-ROM version of the database. The MIT-BIH Arrhythmia Database contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Twenty-three recordings wer

Facebook social network - A social friendship network extracted from Facebook consisting of people (nodes) with edges representing friendship ties.

This course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. We will start with essential notions of probability and statistics. We will proceed to cover techniques in modern data analysis: regression and econometrics, design of experiments, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real-world examples and frontier research. Finally, we will provide instruction on the use of the statistical package R, and opportunities for students to perform self-directed empirical analyses.

This data file contains constituency (state-level) returns for elections to the U.S. presidency from 1976 to 2020.

13128 Global import shipment records of Mit with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset contains all the images from MIT-Adobe FiveK Dataset in .jpg format. All the images in folder 'raw' are directly converted from the raw files. Folder 'a', 'b', 'c', 'd', 'e' contain retouched images by 5 different experts.

We collected 5,000 photographs taken with SLR cameras by a set of different photographers. They are all in RAW format; that is, all the information recorded by the camera sensor is preserved. We made sure that these photographs cover a broad range of scenes, subjects, and lighting conditions. We then hired five photography students in an art school to adjust the tone of the photos. Each of them retouched all the 5,000 photos using a software dedicated to photo adjustment (Adobe Lightroom) on which they were extensively trained. We asked the retouchers to achieve visually pleasing renditions, akin to a postcard. The retouchers were compensated for their work.

LICENSE: see the official website of MIT-Adobe FiveK Dataset.

This data contains a regional implementation of the Massachusetts Institute of Technology general circulation model (MITgcm) at a 1-km spatial resolution for the -158 - -154º W, 18.2-20.8º N region. Variables available are temperature, salinity, zonal, meridional and vertical velocity of currents, for 35 depth levels between the surface and 1000m.

Context This is the Original data provided by MIT .

Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g., bookstores) are better characterized by the objects they contain. More generally, to address the indoor scenes recognition problem we need a model that can exploit local and global discriminative information.

Content The database contains 67 Indoor categories, and a total of 15620 images. The number of images varies across categories, but there are at least 100 images per category. All images are in jpg format. The images provided here are for research purposes only.

Acknowledgements Thanks to MIT Thanks to Aude Oliva for helping to create the database of indoor scenes. Funding for this research was provided by NSF Career award (IIS 0747120)

A beginner-friendly version of the MIT-BIH Arrhythmia Database, which contains 48 electrocardiograms (EKGs) from 47 patients that were at Beth Israel Deaconess Medical Center in Boston, MA in 1975-1979.

There are 48 CSVs, each of which is a 30-minute echocardiogram (EKG) from a single patient (record 201 and 202 are from the same patient). Data was collected at 360 Hz, meaning that 360 data points is equal to 1 second of time.

Banner photo by Joshua Chehov on Unsplash.

How to Analyze the Heart with Python

1. How to Analyze Heartbeats in 15 Minutes with Python
2. How the Heart Works (and What is a "QRS" Complex?)
3. How to Identify and Label the Waves of an EKG
4. How to Flatten a Wandering EKG
5. How to Calculate the Heart Rate

What is a 12-lead EKG?

EKGs, or electrocardiograms, measure the heart's function by looking at its electrical activity. The electrical activity in each part of the heart is supposed to happen in a particular order and intensity, creating that classic "heartbeat" line (or "QRS complex") you see on monitors in medical TV shows.

There are a few types of EKGs (4-lead, 5-lead, 12-lead, etc.), which give us varying detail about the heart. A 12-lead is one of the most detailed types of EKGs, as it allows us to get 12 different outputs or graphs, all looking at different, specific parts of the heart muscles.

This dataset only publishes two leads from each patient's 12-lead EKG, since that is all that the original MIT-BIH database provided.

What does each part of the QRS complex mean?

Check out Ninja Nerd's EKG Basics tutorial on YouTube to understand what each part of the QRS complex (or heartbeat) means from an electrical standpoint.

Filenames

Each file's name is the ID of the patient (except for 201 and 202, which are the same person).

Columns

• index
• calculated elapsed milliseconds (index / 360 * 1000)
• the first lead
• the second lead

The two leads are often lead MLII and another lead such as V1, V2, or V5, though some datasets do not use MLII at all. MLII is the lead most often associated with the classic QRS Complex (the medical name for a single heartbeat).

Milliseconds were calculated and added as a secondary index to each dataset. Calculations were made by dividing the index by 360 Hz then multiplying by 1000. The original index was preserved, since the calculation of milliseconds as digital signals processing (e.g. filtering) occurs may cause issues with the correlation and merging of data. You are encouraged to try whichever index is most suitable for your analysis and/or recalculate a time index with Pandas' to_timedelta().

Patient information

Info about each of the 47 patients is available here, including age, gender, medications, diagnoses, etc.

Getting Started

Physionet has some online tutorials and tips for analyzing EKGs and other time series / digital signals.

Check out our notebook for opening and visualizing the data.

How the CSVs were obtained

A write-up on how the data was converted from .dat to .csv files is available on Medium.com. Data was downloaded from the MIT-BIH Arrhythmia Database then converted to CSV.

Citations

Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001). (PMID: 11446209)

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

Artificial intelligence (AI) based automated epilepsy diagnosis has aimed to ease the burden of manual detection, prediction, and management of seizure and epilepsy-specific EEG signals for medical specialists. With increasing open-source, raw, and large EEG datasets, there is a need for data standardization of patient and seizure-sensitive AI analysis with reduced redundant information. This work releases a balanced, annotated, fixed time and length meta-data of CHB-MIT Scalp EEG database v1.0.0.0.

The work releases patient-specific (inter and intra) and patient non-specific EEG data extracted using specific time stamps of ictal, pre-ictal, post-ictal, peri-ictal, and non-seizure EEG provided in the original dataset (annotations). Further details of this metadata can be found in the provided csv file (CHB-MIT DB timestamp.csv). The released EEG data is available in csv format and class labels are provided in the last row of the csv files. Data of ch06, ch12, ch23, and ch24 in patient-specific and chb24_11 in patient non-specific have not been included. The importance of peri-ictal EEGs has been elucidated in Handa, P., & Goel, N. (2021). Peri‐ictal and non‐seizure EEG event detection using generated metadata. Expert Systems, e12929.

This dataset was developed as part of the DAF-MIT Artificial Intelligence Accelerator to support research on airborne magnetic anomaly navigation (MagNav), an alternative to GPS. Flight data was collected during the summer of 2020 by Sander Geophysics Ltd. (SGL) near Ottawa, Ontario, Canada using a Cessna Grand Caravan equipped with a number of sensors.

Within the dataset is a nearly perfect signal of the Earth's magnetic field (minimal aircraft interference) from tail stinger (boom) measurements, as well as four noisy magnetic signals (varying degrees of aircraft interference) from in-aircraft measurements. These scalar measurements of the total field were generated from five optically pumped, split-beam cesium vapor magnetometers. Additionally, four fluxgate magnetometers were used for vector measurements of the total field. The dataset also contains supplemental sensor data from the inertial navigation system, GPS position data, voltages, currents, and more.

During the data collection flights, various events were purposely carried out to cause temporal magnetic field disturbances. This included control surface movements (e.g., flaps up/down), fuel pump on/off, radio use, and movement of magnetic materials within the cabin. The flight patterns and altitudes were also varied from flight to flight to provide a diverse dataset. Please see the provided datasheet for further high-level dataset information and the readmes for individual flight details.

As of release v3 this data includes both year 1 of the collection (Flights 1002-1007) and year 2 of the collection (Flights 2001-2017) which correspond to 2020 and 2021 respectively. The zip files that are now available include information about the various fields available in each collection with the flight specific readmes including flight plan information (survey line vs transit, etc).

This dataset supports the Signal Enhancement for Magnetic Navigation Challenge Problem: https://magnav.mit.edu

The original and noise-attenuated ECG signals obtained from the MIT-BIH dataset are used as a data source to classify five types of arrhythmias according to the Association for the Advancement of Medical Instrumentation (AAMI) standard EC57. The MIT-BIH dataset contains 48 ECG recordings, thirty minutes long, obtained from 47 patients. Each record has two types of ECG signals: Lead II and Lead V5. The recordings were digitized across a 10 mV range at 360 Hz per channel in 11-bit resolution. In this experiment, ECGs Lead II have been extracted, scaled, and segmented into five types of arrhythmias including.

fusion (F) = 0, normal (N) = 1, unknown beat (Q) = 2. supraventricular-ectopic (S) = 3, ventricular-ectopic (V) = 4

The python code can be found in the following link. https://www.kaggle.com/talal92/fork-of-mit-org-data-preprocess

Please cite and visit our papers for more details. 1. Classifying Cardiac Arrhythmia from ECG Signal Using 1D CNN Deep Learning Model. 2. Classification of cardiac arrhythmia using a convolutional neural network and bi-directional long short-term memory 3. Explainable Deep Learning Model for Cardiac Arrhythmia Classification

MIT Environmental Impulse Response Dataset The audio recordings in this dataset are originally created by the Computational Audition Lab at MIT. The source of the data can be found at: https://mcdermottlab.mit.edu/Reverb/IR_Survey.html. The audio files in the dataset have been resampled to a sampling rate of 16 kHz. This resampling was done to reduce the size of the dataset while making it more suitable for various tasks, including data augmentation. The dataset consists of 271 audio files… See the full description on the dataset page: https://huggingface.co/datasets/davidscripka/MIT_environmental_impulse_responses.

user-calls-user - Reality mining network data consists of human mobile phone call events between a small set of core users at the Massachusetts Institute of Technology (MIT) whom actually were assigned mobile phones for which all calls were collected. The data also contains calls from users outside this small set of users to other phones of individuals that were not actively monitored and thus these nodes generally have fewer edges than nodes within the small set of users at MIT that participated in the experiment and were assigned phones. The data was collected collected by the Reality Mining experiment performed in 2004 as part of the Reality Commons project. The data was collected over 9 months using 100 mobile phones. A node represents a person; an edge indicates a phone call or voicemail between two users. See http://realitycommons.media.mit.edu/realitymining.html for more details.

13128 Global export shipment records of Mit with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

City of Cambridge, MA GIS basemap development project encompasses the land area of City of Cambridge with a 200 foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale.

lamm-mit/bio-silk-mech-data-integrated dataset hosted on Hugging Face and contributed by the HF Datasets community

Five open ECG databases from PhysioNet are involved in this study namely the MIT-BIH arrhythmia database,St-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database,The MIT-BIH Normal Sinus Rhythm Database,The MIT-BIH Long Term Database and European ST-T Database.