Toward automated classification of pathological transcranial Doppler waveform morphology via spectral clustering

Figures

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  Fig 1. Cluster features. Cluster features are depicted for three example waveforms taken from each of the subject groups; OHC (A), IHC (B), and LVO (C). All waveforms were normalized in both time and velocity, so as to span 100 total samples ranging from zero to one on the y-axis. The onset variable (vertical line) marks the time sample where maximum velocity is attained. The canopy variable (horizontal line) marks the length (in samples) of the systolic canopy. The peaks variable is a weighted sum of waveform peaks, both true (indicated by circles) and pseudo (triangles).

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  Fig 2. Cluster dispersion and gap statistic. Pooled Intra-cluster dispersion (A), and associated Gap Statistics (B) as a function of cluster number. Gap-statistic disparity was maximized at four clusters which also corresponded to the optimal number given by the selection criteria in Tibshirani et al. [19].

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  Fig 3. Cluster feature space. Three-dimensional feature space is shown in Z-scored coordinates with the optimal four clusters indicated by color. Associated cluster morphologies are shown for each beat type (B-E), with each cluster archetype shown in color, and the range of the next 50 most representative exemplars depicted in gray. Associated histograms in the upper right (B-E) demonstrate that Type I and II clusters were primarily composed of waveforms from control patient populations, whereas Type III and IV clusters were primarily composed of LVO patient waveforms.

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  Fig 4. Example waveforms. The individual example waveforms most representative of each cluster are shown in standard units (unnormalized), with variability across individual beats depicted in light gray. The Types I and II examples (A and B) originated from the OHC and IHC groups, respectively, whereas the Types III and IV examples (C and D) originated from LVO, and IHC subjects, respectively.