Automated feature extraction from large cardiac electrophysiological data sets

从大型心脏电生理数据集中自动提取特征

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作者:John Jurkiewicz, Stacie Kroboth, Viviana Zlochiver, Peter Hinow

Conclusions

Our work contributes towards a non-invasive approach for cardiomyocyte functional maturation, as well as developmental, pathological and pharmacological studies. As the human-derived cardiac model tissue has the genetic makeup of its donor, a powerful tool for individual drug toxicity screening emerges.

Objective

We set out to develop an algorithm capable of automatically extracting regions of high-quality action potentials from terabyte size experimental

Results

Our automatic segmentation algorithm finds regions of acceptable action potentials in large data sets of electrophysiological readings. We use spectral methods and support vector machines to classify our readings and to extract relevant features. We are able to show that action potentials from the same cell site can be recorded over days without detrimental effects to the cell membrane. The variability between measurements 24 h apart is comparable to the natural variability of the features at a single time point. Conclusions: Our work contributes towards a non-invasive approach for cardiomyocyte functional maturation, as well as developmental, pathological and pharmacological studies. As the human-derived cardiac model tissue has the genetic makeup of its donor, a powerful tool for individual drug toxicity screening emerges.

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