In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.

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Models that represent the mechanisms that initiate and sustain atrial fibrillation (AF) in the heart are computationally expensive to simulate and therefore only capture short time scales of a few heart beats. It is therefore difficult to embed biophysical mechanisms into both policy-level disease models, which consider populations of patients over multiple decades, and guidelines that recommend treatment strategies for patients. The aim of this study is to link these modelling paradigms using a stylised population-level model that both represents AF progression over a long time-scale and retains a description of biophysical mechanisms. We develop a non-Markovian binary switching model incorporating three different aspects of AF progression: genetic disposition, disease/age related remodelling, and AF-related remodelling. This approach allows us to simulate individual AF episodes as well as the natural progression of AF in patients over a period of decades. Model parameters are derived, where possible, from the literature, and the model development has highlighted a need for quantitative data that describe the progression of AF in population of patients. The model produces time series data of AF episodes over the lifetimes of simulated patients. These are analysed to quantitatively describe progression of AF in terms of several underlying parameters. Overall, the model has potential to link mechanisms of AF to progression, and to be used as a tool to study clinical markers of AF or as training data for AF classification algorithms.

Context

• blog: https://medium.com/@coreyauger/part-4-searching-for-signals-5eaa2432e757
• site: http://daytrader.ai

Day traders identify patterns in the market that tell them when to enter and exit a trade. They never hold market position over night meaning profit targets for trades are expressed in minutes not days. To further back their technical analysis they view the data in multiple time domains trying to locate so called "support" and "resistance" levels. Many of the technical indicators are based on price alone.

This data set should allow a machine algorithm to form better technical indicators then a human, thus allowing it to predict probabilities for entry conditions better then a human day trader.

For this data set we analysed 7 years of NASDAQ100 data (from 2010 to mid 2017). Every morning we wait until 90 minutes of trading history has occurred before scanning for a simple pattern. This pattern is when the 15 minute EMA crosses over the 65 minute EMA.

Symbols included in the search:

• FB - Facebook
• BABA - Alibaba
• GOOG - Google class C
• AAPL - Apple
• TSLA - Tesla
• MSFT - Microsoft
• NVDA - NVidia
• AMZN - Amazon
• CRM - Salesforce
• GOOGL - Google class A
• ADBE - Adobe
• NFLX - Netflix
• INTC - Intel
• BIDU - Baidu

Content

Once the pattern has been detected I give you 2400 minute (40 hours) of previous history. As well as 20 minutes of future history.
The data will be formatted as follows.

File name: data_**N**_**SYM**.csv

• N is an incremental integer
• SYM is the stock symbol ticker (FB, BABA, ect..)

Inside each of the csv files you will find 2420 lines of comma separated values, with format:

ISO formatted date, closing price, volume.

Eg:

2017-10-17T14:18:00.000Z,201.87,55800.0
2017-10-17T14:19:00.000Z,201.21,137786.0
2017-10-17T14:20:00.000Z,201.852,103695.0
2017-10-17T14:21:00.000Z,201.6,81362.0
2017-10-17T14:22:00.000Z,201.54,30183.0
2017-10-17T14:23:00.000Z,201.43,72405.0
2017-10-17T14:24:00.000Z,201.15,79411.0
2017-10-17T14:25:00.000Z,201.48,125713.0

The task should report a probability that this will be a successful trade or not.

Further note: One should keep in mind that there are trading fees involved for the entry and exit of the trade. So in order to profile you will need to beat this spread.

Acknowledgements

Further ideas and questions can be directed to http://daytrader.ai Thanks and I hope you have some fun with this set :) blog: https://medium.com/@coreyauger/daytrader-ai-machine-learning-applied-to-intraday-trading-a6b4e44b0274

Inspiration

The task should try to predict the label with the lowest possible error Useful links:

• Technical Analysis vs Fundamental Analysis
• https://medium.com/@coreyauger/daytrader-ai-machine-learning-applied-to-intraday-trading-a6b4e44b0274

This library comes from the Million Song Dataset, which used a company called the Echo Nest to derive data points about one million popular contemporary songs. The Million Song Dataset is a collaboration between the Echo Nest and LabROSA, a laboratory working towards intelligent machine listening. The project was also funded in part by the National Science Foundation of America (NSF) to provide a large data set to evaluate research related to algorithms on a commercial size while promoting further research into the Music Information Retrieval field. The data contains standard information about the songs such as artist name, title, and year released. Additionally, the data contains more advanced information; for example, the length of the song, how many musical bars long the song is, and how long the fade in to the song was.

Data Dictionary

...

Key	List of...	Comment	Example Value
artist.familiarity	Float	A measure of 0..1 for how familiar the artist is to listeners.	0.581793766
artist.hotttnesss	Float	A measure of the artists's popularity, when downloaded (in December 2010). Measured on a scale of 0 to 1.	0.401997543
artist.id	String	A unique ID for this artist.	"ARD7TVE1187B99BFB1"
artist.latitude	Float	The home location's latitude of this artist.	0.0
artist.location	Integer	Unknown.	0
artist.longitude	Float	The home location's longitude of this artist.	0.0
artist.name	String	The name of the artist.	"Casual"
artist.similar	Float	Unknown.	0.0
artist.terms	String	The term most associated with this artist.	"hip hop"
artist.terms_freq	Float	The frequency of this term.	1.0
release.id	Integer	The ID of the release (album) on the service 7digital.com	300848
release.name	Integer	Unknown value	0
song.artist_mbtags	Float	Unknown field.	0.0
song.artist_mbtags_count	Float	Number of tags for the artist on mbtags.	0.0
song.bars_confidence	Float	Confidence value (between 0 and 1) associated with each bar.	0.643
song.bars_start	Float	Average start time of each bar, measured in bars.	0.58521
song.beats_confidence	Float	Average confidence interval of the beats.	0.834
song.beats_start	Float	Average start time of each beat, measured in beats.	0.58521
song.duration	Float	Duration of the track in seconds.	218.93179
song.end_of_fade_in	Float	Time of the end of the fade in, at the beginning of the song.	0.247
song.hotttnesss	Float	A measure of the song's popularity, when downloaded (in December 2010). Measured on a scale of 0 to 1.	0.60211999
song.id	String	A uniquely identifying number for the song.	"SOMZWCG12A8C13C480"
song.key	Float	Estimation of the key the song is in. Keys can be from 0 to 11.	1.0
song.key_confidence	Float	Confidence value (between 0 and 1) of the key estimation.	0.736
song.loudness	Float	General loudness of the track	-11.197
song.mode	Integer	Estimation of the mode the song.