Benchmark on a large cohort for sleep-wake classification with machine learning techniques

Accurately measuring sleep and its quality with polysomnography (PSG) is an expensive task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However, the largest experiments conducted to date, have had only hundreds of participants. In this work, we processed the data of the recently published Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study to have both PSG and actigraphy data synchronized. We propose the adoption of this publicly available large dataset, which is at least one order of magnitude larger than any other dataset, to systematically compare existing methods for the detection of sleep-wake stages, thus fostering the creation of new algorithms. We also implemented and compared state-of-the-art methods to score sleep-wake stages, which range from the widely used traditional algorithms to recent machine learning approaches. We identified among the traditional algorithms, two approaches that perform better than the algorithm implemented by the actigraphy device used in the MESA Sleep experiments. The performance, in regards to accuracy and F1 score of the machine learning algorithms, was also superior to the device’s native algorithm and comparable to human annotation. Future research in developing new sleep-wake scoring algorithms, in particular, machine learning approaches, will be highly facilitated by the cohort used here. We exemplify this potential by showing that two particular deep-learning architectures, CNN and LSTM, among the many recently created, can achieve accuracy scores significantly higher than other methods for the same tasks.Other Information Published in: npj Digital Medicine License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1038/s41746-019-0126-9</p

The heatmap shows 74 HGCs absent from reference strains Mtb ATCC H37Ra and Mtb ATCC H37Rv but present in most of the clinical isolates.

<p>Predicted annotation of each of the cluster family is also represented. The genes validated using PCR are also shown adjacent to the annotations.</p

Molecular function GO annotations of the soft-core component.

<p>GO annotation for Biological process and Cellular component is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122979#pone.0122979.s005" target="_blank">S5</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122979#pone.0122979.s006" target="_blank">S6</a> Figs.</p

Genome Assembly Statistics of the eight Indian strains sequenced and assembled as part of the present study.

<p>Genome Assembly Statistics of the eight Indian strains sequenced and assembled as part of the present study.</p

Summary of sequence annotation statistics from BLAST2GO.

<p>Representative sequences from all the 8,099 HGCs were subjected to annotation out of which 47.77% (3,869) sequences were annotated with GO slim terms, 26.63% (2,157) sequences were without any BLAST hits, 23.43% (1,898) sequences had only blast results but didn’t had annotation and 1.65% (134) sequences retrieved mapping results but were without GO slim terms. A small fraction of 0.5% (41) sequences failed to fetch BLAST results.</p

Overlap of accessory orthologous clusters shared within each strain pair.

<p>The diagonal represent the number of clusters present in any given strain and divides the data into exactly similar halves. Color key indicates the distribution of clusters. The sharing is irrespective of the cluster being present in any other strain. The minimum number of shared clusters is 109 clusters between Mcan CIPT 140070008 and Mbovis BCG Phipps and maximum (521) is between Mbovis BCG Sweden and Mbovis BCG Prague9.</p

Core and accessory genome size evolution.

<p>(A) Each point indicates the number of HGCs conserved in a genome. The red line indicates an exponential decay function based on the median values of core HGCs when each time a new genome is added to the analysis. (B) Accessory genome of MTBC. The MTBC has an open pangenome model.</p

(A) PCR validation of the 10 candidate genes (subset of 74 clusters identified) in 8 Indian clinical isolates (OSDD strains) and reference strains ATCC H37Ra and ATCC H37Rv.

<p>Arrow heads indicate the variably sized products in ATCC H37Ra and ATCC H37Rv genomes. Gene 4 and Gene 8 shares partial homology with laboratory strains and showed the presence of a differently sized product in ATCC H37Rv and ATCC H37Ra. (B) The alignment of Gene 4 against reference genomes showed an insertion of 175bp sequence in Gene 4 of OSDD strains with respect to reference strains. (C) The alignment of Gene 8 against reference genomes showed a deletion of 1,352bp sequence in Gene 8 of OSDD strains.</p

Average methylation density at the intron-exon-intron junctions.

<p>Distribution of peak summit count in 10 bp sliding window, 200 bp upstream and downstream from the start site and end site of exons was calculated for all RefSeq gene exons, first exon and all last exons. Smoothing of peaks was done by taking moving average of 5.</p

Methylation marks the coding region.

<p>Third exon of the <i>Ccdc 75</i> gene shows methylation in MeDIP-Seq data as visualized in UCSC genome browser.</p