A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis

Functional clustering of neuronal signals with FMM mixture models

“…In addition, positive or negative spikes are recognize with values of β far from 0 and π , respectively. On the other hand, the questions of high channel-count analysis and spike shape variation caused by electrode drift can be addressed with the aid of recent results in [45] , [46] . These papers present the adequate multicomponent and multidimensional FMM model when sequential events are observed from different locations.…”

“…Hence, only these last parameters must be analyzed to identify possible electrode drift by comparing the values of these parameters for typical spikes with those of the same individual recorded in other experiments. In [45] , multi-lead ECG signals are analyzed. That paper shows how the multidimensional and multicomponent FMM model can identify spikes common to different channels, even if the amplitude is too small in some of them or the waveform is corrupted by noise.…”

Direct Estimation of Equivalent Bioelectric Sources Based on Huygens’ Principle

“…The potential to be able to characterize the electric activity of different organs using a non-invasive method for the computation of their surface potentials is a major breakthrough, especially in real-time applications (i.e., diagnosis of a cardiac arrest). Such non-invasive methods are commonly known as the inverse problem of electroencephalography [ 3 , 13 , 14 ] and electrocardiography [ 15 , 16 , 17 ], respectively, which have been studied to a great extent within the scientific community. Inverse problems aim at the specification of those equivalent sources responsible for the genesis of several known (measured) potentials on a surface that encloses them.…”