The Data set consists of 1200 records of Cardiovascular ECGs where each of the 300 records belongs to one ailment, in such a way 4 ailments have been considered. The original signals are taken from the MIT-BIH physio-net Database. One ailment is the MIT-BIH Arrhythmia Database, the other is BIDMC Congestive Heart Failure Database and MIT-BIH Atrial Fibrillation Database and finally MIT-BIH Normal Sinus Rhythm Database. From these four databases, ECG records have been segmented at 4120 samples each forming 300 signals. They are normalized with mentioned gain for each database and are preprocessed with bandpass filters. MODWPT technique was used to obtain 54 features that are given as columns in .csv file that is uploaded here. So the file has 1200 x 54 size records.
Note:: Missing values have to be handled according to your application.

ACKNOWLEDGEMENT
Please credit the authors if you use this dataset file in your research.

Citation:

1. Alekhya, L., and P. Rajesh Kumar, "A new approach to detect cardiovascular diseases using ECG scalograms and ML-based CNN algorithm." Mar 20, 2023. International Journal of Computational Vision and Robotics/Inderscience publishers.
   DOI: 10.1504/IJCVR.2022.10051429
   Link: https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=IJCVR

2. Alekhya, L., and P. Rajesh Kumar. "A Novel Application for Autonomous Detection of Cardiac Ailments using ECG
   Scalograms with Alex Net Convolution Neural Network." Design Engineering (2021): 13176-13189.
   Link: http://www.thedesignengineering.com/index.php/DE/article/view/6434

3. Autonomous Detection of Cardiac Ailments using Long-short term Memory Model based on Electrocardiogram signals, L. Alekhya, P. Rajesh Kumar, A. Venkata Sriram
   DOI: 10.14704/nq.2022.20.7.NQ33431. Pages: 3509 - 3518.
   Link: https://www.neuroquantology.com/open-access/Autonomous+Detection+of+Cardiac+Ailments+using+Longshort+term+Memory+Model+based+on+Electrocardiogram+signals_5781/

4. Autonomous Detection of Cardia Ailments diagnosed by Electrocardiogram using various Supervised Machine Learning AlgorithmsAutonomous Detection of Cardia Ailments diagnosed by Electrocardiogram using various Supervised Machine Learning Algorithms
   AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) · Sep 18, 2021.
   Link: https://www.shin-norinco.com/article/autonomous-detection-of-cardia-ailments-diagnosed-by-electrocardiogram-using-various-supervised-machine-learning-algorithms

5. L Alekhya, P Rajesh Kumar, “Maximal Overlap Discrete Wavelet Packet Transform Based Characteristic waves detection in Electrocardiogram of Cardiovascular Diseases”, INTERNATIONAL JOURNAL OF SPECIAL EDUCATION, vol 36 (1), pp 51-61, 2021.

License
License was not specified at the source, yet access to the data is public and a citation was requested.