Machine learning for automatic prediction of the quality of electrophysiological recordings

The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80–90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters

Effect of bronchial asthma education program on asthma control among adults at Mansoura district

Background: Bronchial asthma is one of the most common causes for intensive care units visits and admission for medical seeks each year (Arts, 2013). Proper education programs for asthma are necessary to increase asthma knowledge and asthma control. Aim and objectives: This study aimed to improve bronchial asthma control in all patients in family health care units. This study planned and implemented the bronchial asthma educational program and evaluated the level of asthma symptoms control after education program among adult asthmatic patients. Patients and methods: The Short term intervention study was carried out at 7 accredited family health care units at Mansoura district from February 2016 to July 2016. Eighty-four patients (45 females and 39 males) chosen by systematic random technique and already diagnosed bronchial asthma, were included in this study. Intervention analysis were done consisting of sessions of education for each group of patients at their family health care unit. Asthma control questionnaire (ACQ) was obtained every 2 weeks for 3 months. Results: The study included 84 asthmatic patients their mean age was 37 years old, (72.7%) had moderate asthma. The educational intervention significantly improved the level of asthma knowledge and level of asthma control (p < 0.001), has led to fewer visits to the emergency room, hospitalization and referral to specialist (p < 0.001). Conclusion: Asthma education provided by the family physicians is needed to improve symptoms control and to increase the level of asthma knowledge in adult patients with mild to moderate asthma

Illustration of the calculation of spike height, spike width and noise amplitude.

<p>A) Spike height and width: The blue trace represents the original voltage data with small blue markers indicating the sampling. The red line is the moving average, which is used in spike detection. The black horizontal line represents the baseline value that is calculated by averaging the membrane potential in windows to the left and the right of the spike. The spike height is determined as the difference of the maximal voltage value of the spike and the baseline value. The spike width is measured as the distance of the two closest measurements below the half-height of the spike. B) Short time scale noise amplitude: The difference is taken between the original membrane potential measurement <i>V</i>_m and the filtered membrane potential measurement <i>V</i>_avg (moving average, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080838#pone-0080838-g001" target="_blank">Figure 1A</a>), and its Euclidean norm (normalised by 2 times the filter length +1) is calculated over two filter lengths, (1) e.g. the noise level at the arrowhead is calculated from the marked interval of 2 filter lengths. The areas of 2 maximal spike widths (2×3 ms) around every detected spike are excluded from this calculation and the local noise level is undefined in these areas around spikes.</p

Human and machine judgements on data set 1.^a

a<p>Correlation between the prediction vectors (bad = −1, intermediate = 0, good = 1).</p

Comparison of individual predictions of expert 1 and the machine learning classifier 6–2133.

<p>(The feature sets are labeled by their size, here 6 features, and then enumerated from 0 to N–1, where N is the maximal number of feature choices for the given size. The feature set used here is number 2133 out of a total of 8008 possible choices.) for data set 1. A) Match of the predictions of human expert and machine. The recordings are ordered from the recording with the worst mismatch between expert 1 and the 50 times repeated 10-fold crossvalidation with 6–2133 on the left to the ones with the least mismatch on the right. The colour represents the percentage of crossvalidation runs where machine and human prediction match scaling from blue (0% match) to dark red (100% match). The 3-row prediction matrices show the details of how often individual recordings (x-axis) were recognised as the three classes (y-axis). The colour scale is the same as for the performance. B)–G) Raw data plots of the three most problematic recordings, where blue lines are the voltage data, red lines the filtered voltage data, red dots the top of detected spikes, and green dots the half-height of spikes, shown at spike time +/− half spike width. Panels C and D are detailed plots of two relevant regions of recording #92 as indicated by arrowheads in B. The inset in F and panel G are details of two relevant regions of recording #89 as indicated by arrowheads in F.</p

Overview of the observed distributions of feature values for data set 1.

<p>The value distributions are shown separately for recordings that were classified as good (red), intermediate (green) or bad (blue) by expert 1. We have compared the distributions with Kolmogorov-Smirnov tests and found that many but not all distributions differ significantly on significance level α = 0.05 (one star) or α = 0.01 (two stars). We note that the distributions between intermediate and bad recordings rarely differ significantly but often both do differ from the good recordings.</p

Distributions of the occurrence of individual features in the top10 feature groups for each given size of the feature set from 1 (top left panel) to 16 (bottom right panel) features.

<p>If a single feature is used, feature 4 (CV of spike width) is best for data set 1 (A) and feature 14 (minimum spike slope) for data set 2 (B), but when using 2 or more features, combinations involving other features work best. The highly successful feature sets between 5 to 10 features (second row) show some commonalities in that for data set 1 (A) features 8 (std of noise level) and 13 (maximum spike slope) are always included and for data set 2 (B) features 1 (mean spike height), 3 (CV of spike height), 8 (std of noise level) and 14 (minimum spike slope) play a dominant role. It is noteworthy that observations reported here do not seem to generalize well between the two data sets used in this study.</p

Overview of the observed distributions of feature values for data set 2.

<p>The conventions are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080838#pone-0080838-g003" target="_blank">Figure 3</a>. We note that for this data set the differences of feature value distributions are even more pronounced.</p

Full set of considered features of recordings.

<p>Full set of considered features of recordings.</p

Human and machine judgements on data set 2.^a

a<p>Correlation between the prediction vectors (bad = −1, intermediate = 0, good = 1).</p>b<p>The last column and row show the correlation to the result of feature selection and training on data set 1 and then predicting data set 2 with all members of the top10 group of size 13 (the one performing best, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080838#pone-0080838-g008" target="_blank">Figure 8</a>).</p