The growing technical standard of acquisition systems allows the acquisition of large records, often reaching gigabytes or more in size as is the case with whole-day electroencephalograph (EEG) recordings, for example. Although current 64-bit software for signal processing is able to process (e.g. filter, analyze, etc) such data, visual inspection and labeling will probably suffer from rather long latency during the rendering of large portions of recorded signals. For this reason, we have developed SignalPlant-a stand-alone application for signal inspection, labeling and processing. The main motivation was to supply investigators with a tool allowing fast and interactive work with large multichannel records produced by EEG, electrocardiograph and similar devices. The rendering latency was compared with EEGLAB and proves significantly faster when displaying an image from a large number of samples (e.g. 163-times faster for 75  ×  10(6) samples). The presented SignalPlant software is available free and does not depend on any other computation software. Furthermore, it can be extended with plugins by third parties ensuring its adaptability to future research tasks and new data formats.

The presented solution achieved 7th place out of 48 competing entries in the Physionet Challenge 2016 (official phase). In addition, the PROBAfind software for probability assessment was introduced.

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Aims: According to the "2016 Physionet/CinC Challenge", we propose an automated method identifying normal or abnormal phonocardiogram recordings. Method: Invalid data segments are detected (saturation, blank and noise tests). The record is transformed into amplitude envelopes in five frequency bands. Systole duration and RR estimations are computed; 15-90 Hz amplitude envelope and systole/RR estimations are used for detection of the first and second heart sound (S1 and S2). Features from accumulated areas surrounding S1 and S2 as well as features from the whole recordings were extracted and used for training. During the training process, we collected probability and weight values of each feature in multiple ranges. For feature selection and optimization tasks, we developed C# application PROBAfind, able to generate the resultant Matlab code. Results: The method was trained with 3153 Physionet Challenge recordings (length 8-60 seconds; 6 databases). The results of the training set show the sensitivity, specificity and score of 0.93, 0.97 and 0.95, respectively. The method was evaluated on a hidden Challenge dataset with sensitivity and specificity of 0.77 and 0.91, respectively. These results led to an overall score of 0.84.

Introduction: An analysis of ultra-high-frequencies in ECG (UHF ECG, up to 2 kHz) reveals new information about the time spatial distribution of heart depolarization. Such an analysis may be important for diagnosing and treating patients with atrial and ventricular dyssynchrony. The UHF analysis in patients with a pacing device is complicated due to the pacing influence in the ECG. In that case, all pacing artefacts must be eliminated from the measured signal. The first step in removing those artefacts is to precisely detect their temporal position. Although pacing artefacts are usually clearly visible on a measured ECG, capturing the whole pacing artefact may be challenging.Methods: This paper compares different detection approaches and evaluates them on 19 records. Derivatives, a moving statistical window and complex envelope methods were tested followed by descriptive statistics approaches for making a peak detection. We evaluated the variability of the detection position by the distance variability from manual anotations. For each method, sensitivity and positive predictivity were evaluated.Results: The method with the most precise temporal detection was the variance moving window with a standard deviation (SD) of ±0.11 ms mark placement. The best detection method was a SD moving window with sensitivity=100 and specificity=82.3 and was evaluated as the most appropriate.