The electrocardiogram (ECG) is a graphic representation of the electrical activity of the heart. It is recorded from the body surface using a number of electrodes placed in specific predefined areas. It is considered as a fundamental tool of clinical practice. It is a simple, non-invasive exam that can be performed by any health professional. Placing the electrodes is not considered as a medical procedure, yet in some countries, the prescription of the ECG by a doctor is essential for it to be performed. The ECG constitutes the first step in cardiovascular diseases (CVD) diagnosis, and is used multiple times throughout the life of a CVD patient, CVD constitute the first global cause of death.
An ECG is composed of multiple temporal signals, called lead signals, such as the standard 12-lead ECG shown in FIG. 1. An ECG displays repeating patterns usually comprising a P-wave, a QRS complex and a T-wave, respectively corresponding to the depolarization of the atria, depolarization of the ventricles and repolarization of the ventricles. These waves and complex are shown in FIG. 2, which focuses on a couple of beats in one lead signal.
The ECG allows for the detection of many anomalies, which often in turn point to specific CVD. It is estimated that about 150 measurable anomalies can be identified on ECG recordings today. However, without specific expertise and/or regular training, only a small portion of these anomalies can be easily spotted. Unfortunately, today, it is estimated that only one third of ECGs are performed in settings where cardiology expertise is readily available.
In order to make ECO interpretation simpler and assist non-specialists, two alternatives exist today, but neither fully satisfy the needs of health professionals:                Telecardiology centers, where an interpretation of an ECG sent by a non-specialist is delivered either by cardiologists or by specialized ECG technicians. Their interpretations am of high quality but are slow and expensive to obtain.        Prior art automated ECG interpretation softwares, which are mainly developed by ECG device manufacturers. They provide low quality interpretation (false alarms are very frequent) but deliver them in seconds.        
Prior art automated ECG interpretation softwares can provide two types of information about an ECG signal:                the temporal locations of each wave, called its “delineation”, and/or        a classification of the ECG as normal/abnormal or labeling its anomalies.        
Two main approaches are used for the delineation of ECG signals.
The first one is based on multiscale wavelet analysis. This approach looks for wavelet coefficients reaching predefined thresholds at well-chosen scales (Martinez et al, IEEE transactions on biomedical engineering. Vol. 51, No. 4, April 2004, 570-581, Almeida et al., IEEE transactions on biomedical engineering, Vol. 56, No. 8, August 2009, pp 1996-2005, Boichat et al., Proceedings of Wearable and Implantable Body Sensor Networks, 2009, pp 256-261, U.S. Pat. No. 8,903,479, 2014 Dec. 2, Zoicas et al.). The usual process is to look for QRS complexes, and then look for P waves on the signal before the complexes, and after them for T waves. This approach can only handle a single lead at a time, sometimes using projection to one artificial lead (US 2014/0148714-2014-05-29, Mamaghanian et al). This computation is made very unstable by the use of thresholds. The approach is also limited as it can neither deal with multiple P waves nor with “hidden” P waves. A hidden P wave is a P wave which occurs during another wave or complex, such as for example during a T wave.
The second one is based on Hidden Markov Models (HMM). This machine learning approach considers that the current state of the signal (whether a sample is either part of a QRS complex, a P wave, a T wave or no wave) is a hidden variable that one wants to recover (Coast et al., IEEE transactions on biomedical engineering, Vol. 37, No. 9, September 1990, pp 826-836, Hughes et al., Proceedings of Neural Information Processing Systems, 2004, pp 611-618, U.S. Pat. No. 8,332,017, 2012 Dec. 11, Trassenko et al). To this end, a representation of the signal must be designed using handcrafted “features”, and a mathematical model must be fitted for each wave, based on these features. Based on a sufficient number of examples, the algorithms can learn to recognize each wave. This process can however be cumbersome since the feature design is not straightforward, and the model, usually Gaussian, is not well adapted. Also, none of these works has considered the situation of hidden P waves.
As for anomalies and/or CVD detection, most algorithms use rules based on temporal and morphological indicators computed using the delineation: PR, RR and QT intervals, QRS width, level of the ST segment, slope of the T wave, etc. . . . These rules such as the Minnesota Code (Prineas et al., Springer, ISBN 978-1-84882.777-6, 2009) were written by cardiologists. However, they do not reflect the way the cardiologists analyze the ECGs and are crude simplifications. Algorithms such as the Glasgow University Algorithm are based on such principles (Statement of Validation and Accuracy for the Glasgow 12-Lead ECG Analysis Program, Physio Control, 2009).
More advanced methods use learning algorithms, and are built using a diagnosis and an adequate representation for each ECG they learn from; however, in these methods, once again, it is necessary to seek a representation of the raw data into a space that preserves the invariance and stability properties. Indeed, an ECG signal varies significantly from one patient to another. It is therefore extremely difficult for an algorithm to learn how to discriminate different diseases by simply comparing raw data. A representation which drastically limits this interpatient variability while preserving the invariance within the same disease class must be chosen.
In order to solve the above-mentioned issues, the Applicant turned to architectures called neural network. Such architectures have been intensively studied in the field of imaging (Russakovsky et al., arXiv:1409.0575v3, 30 Jan. 2015), but limitations arose when, very recently, the first scientific teams attempted to apply them to ECGs (Zheng et al., Web-Age Information Management, 2014, Vol. 8485, pp 298-310, Jin and Dong, Science China Press, Vol. 45, No 3, 2015, pp 398-416). Indeed, these prior arts only limit the classification to an attempt to identify normal ECG versus abnormal ECG, or to perform a beat-to-beat analysis. The beat-to-beat analysis adds a preprocessing step while reducing the ability of the neural network to learn some anomalies: rhythm disorders, for example, cannot be identified from the observation of a single beat. In fact, these algorithms only consider single-label classification whereas multi-label classification is essential in ECG interpretation, since one ECG can present several anomalies.
Thus, there is a need for computerized algorithms able to analyze ECG that can:                carry out the analysis without constraints from the ECG recording duration;        carry out the analysis without the need for beat-by-beat processing, or feature extraction;        obtain the delineation of the signal, including identification of hidden P waves;        provide a multi-label classification directly from a full ECG, possibly exhibiting multiple labels;        be fast, stable and reliable.        