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Comparison of specialized actigraphs with wearable devices in quantitative sleep analysis
Čech, Vladimír ; Zvončák, Vojtěch (referee) ; Mikulec, Marek (advisor)
Actigraphy and smart wearable devices provide similar functionality in quantitative sleep analysis. This work aims to verify how common wearable devices stand in comparison with a specialized actigraph used in clinical practice. The actigraph used in this work was Geneactiv Original and it was used to compare the sleep analysis results of eleven commercially available devices. The measurements for nine of the measured devices lasted for one week. Two devices were measured for six days. During sleep, the device was worn on the same hand as the actigraph, and at the same time a sleep diary was kept, in which data on the time of lying down, the time of awakening and the number of awakenings per night were recorded. After a week of measurement, the data obtained from the actigraph were evaluated by software and then compared with the data provided by the second device. In this work, data of total sleep time, sleep onset latency, number of awakenings during the night and sleep efficiency are compared. Ten of the eleven devices measured higher total sleep time and higher sleep efficiency than the actigraph. These devices measured higher total sleep time by 1.21 % – 12.06 % and measured higher sleep efficiency by 2.86 % - 13.86 %. One device, namely Fossil Sport, measured lower total sleep time by 9.02 % and lower sleep efficiency by 9.13 %. Sleep onset latency was the most distinct parameter. Wearable devices measured higher sleep time by 86.72 % – 1225.95 %. Neither device nor actigraph could reliably determine the number of awakenings during the night unless it was a significant physical activity during the night. From the results of the work, it is not possible to say in general that all commercially available devices would be a reliable substitute for actigraphy.
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Identification of sleep disorders based on actigraphy data and sleep diaries
Molík, Miroslav ; Mekyska, Jiří (referee) ; Mikulec, Marek (advisor)
This master’s thesis deals with prediction of Parkinson's disease using sleep parameters from actigraphy and sleep diaries. The goal is to design a machine learning approach, which will be able to recognize pacients suffering from Parkinson's disease. For training dataset supplied by St. Anne's University Hospital Brno was used, which was variously modified for achieving best possible results. These adjustments were evaluated according to the results of the trained models and based on these results, two models (achieving test accuracies of 85 and 82%) were selected.
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Automated diagnosis of sleep disorders using wearable devices
Sigmund, Jan ; Mekyska, Jiří (referee) ; Mikulec, Marek (advisor)
Sleep disorders induce many negative repercussions. Furthermore, research about their connection to cognitive health is increasing in numbers. This thesis concerns detection of poor sleep quality via raw actigraphy data. Existing method for assessing sleep was selected, it’s performance was validated against polysomnography on 27 patients. Used algorithm defines sleep as the absence of change in arm angle. Resulting 81 % sensitivity, 62 % specificity and 78 % accuracy is different from the outcome in the pilot study. Two approaches, to determine sleep quality were used. Both are based on comparing sleep features – first, with National Sleep Foundation recommendations and second, with control group without sleep disorders (7 persons). The goal was to pinpoint the remaining 19 patients with diagnosis. The recommendation for SOL, WASO, NA>5 and SE had higher sensitivity (75 %), lower specificity (71 %) and identical accuracy (74 %). These approaches were then also tested on 7-day actigraphy, consisting of 27 subjects, that are presumed to have prodromal dementia with Lewy bodies. Same principle was applied to try to predict LBD and thereby address the link between sleep quality and neurodegeneration. This resulted in 86 % sensitivity, 38 % specificity and 63 % accuracy. With regard to achieving solid sensitivity in all cases and good accuracy this could be used to indicate sleep quality.
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Advanced methods for sleep quality assessment
Doležalová, Anna ; Králík, Martin (referee) ; Ronzhina, Marina (advisor)
This diploma thesis is focused on advanced sleep assessment using deep learning. Metrics for sleep assessment and their use are described here. There are hearth rate and accelerometer data from Apple Watch used for classification. The basis for the classification was a model composed of 1D convolution networks in combination with recurrent neural network. LSTM and GRU were used as recurrent networks. Models were taught to classify into two, three and five phases. At last the resulting methods are compared.
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System of secured actigraph data transfer and processing
Mikulec, Marek ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
The new Health 4.0 concept brings the idea of combining modern technologies from field of science and technology with research in healthcare and medicine. This work realizes a system of secured actigraph data transfer and preprocessing based on the concept of Health 4.0. The system is successfully designed, implemented, tested and secured. With the help of a non-invasive method of monitoring the movement and temperature of the subject using the GENEActiv actigraph allows the system to securely transfer, process and evaluate the subject's sleep data using the machine learning algorithm XGBoost. The proposed system is in accordance with the valid law of the Czech Republic and meets legal requirements.
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Assessment of the influence of artificial lighting simulating a natural photoperiod and spectrum on the parameters of circadian rhythms of healthy volunteers
Gesztesová, Kristina ; Bendová, Zdeňka (advisor) ; Jelínková, Dana (referee)
The alternation of light and darkness on planet Earth has led to the development of endogenous systems that operate with a period of roughly 24 hours. We refer to these systems as circadian. For optimal functioning of the human circadian system, regular synchronization by an external stimulus is required. Light is a strong stimulus for synchronization for humans, but it should be noted that the outcome of this light synchronization depends critically on a number of factors. These factors include the timing and duration of the light pulse, as well as light parameters like spectrum and intensity. Commonly used interior lighting is often not optimal for the human endogenous circadian system, which is why the alternative of so-called biodynamic lighting (lighting that adequately stimulates the human organism) is being used more lately. As part of the experiment, we verified the effect of the newly created biodynamic lighting on the parameters of the circadian rhythm of healthy volunteers. Using methods of melatonin profile analysis, analysis of temperature records and actigraphy, we confirmed the influence of our experimental lighting.
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Functional movement disorders: pathophysiological mechanisms, diagnostic neurophysiological markers, and new therapeutic approaches
Slovák, Matěj ; Serranová, Tereza (advisor) ; Vevera, Jan (referee) ; Rusina, Robert (referee)
Functional movement disorders (FMD), previously referred as psychogenic, are characterized by inconsistency and incogruence with organic neurological disorders. The original psychological models of FMD were replaced by a neurobiological model of the disease. The identification of neurophysiological correlates of FMD and their comorbidities may add to the so far limited knowledge of the pathophysiology of these disorders. This Thesis focuses on three thematic areas of FMD: 1. assessment of prevalence of comorbid restless legs syndrome (RLS) and periodic limb movements (PLM) as an objective marker of RLS using actigraphy; 2. analysis of reflexive and volitional eye movements using videooculography (VOG); 3. evaluation of emotional arousal objectively by pupillometry and subjectively using affective ratings of emotional pictures. In total, 115 FMD patients and 76 age- and matched healthy controls participated in the studies. 1. FMD patients (N=96) presented with signficantly higher prevalence of RLS (43,8 % vs. 7,9 %, p<0,001) and clinically relevant PLM (20,8 % vs. 2,6 %, p=0,0002) compared to controls. The association of RLS/PLM with FMD raises the possibility of common pathophysiological mechanisms of these conditions and has clinical implications in management of FMD. 2. VOG analyses showed normal...
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Comparison of specialized actigraphs with wearable devices in quantitative sleep analysis
Čech, Vladimír ; Zvončák, Vojtěch (referee) ; Mikulec, Marek (advisor)
Actigraphy and smart wearable devices provide similar functionality in quantitative sleep analysis. This work aims to verify how common wearable devices stand in comparison with a specialized actigraph used in clinical practice. The actigraph used in this work was Geneactiv Original and it was used to compare the sleep analysis results of eleven commercially available devices. The measurements for nine of the measured devices lasted for one week. Two devices were measured for six days. During sleep, the device was worn on the same hand as the actigraph, and at the same time a sleep diary was kept, in which data on the time of lying down, the time of awakening and the number of awakenings per night were recorded. After a week of measurement, the data obtained from the actigraph were evaluated by software and then compared with the data provided by the second device. In this work, data of total sleep time, sleep onset latency, number of awakenings during the night and sleep efficiency are compared. Ten of the eleven devices measured higher total sleep time and higher sleep efficiency than the actigraph. These devices measured higher total sleep time by 1.21 % – 12.06 % and measured higher sleep efficiency by 2.86 % - 13.86 %. One device, namely Fossil Sport, measured lower total sleep time by 9.02 % and lower sleep efficiency by 9.13 %. Sleep onset latency was the most distinct parameter. Wearable devices measured higher sleep time by 86.72 % – 1225.95 %. Neither device nor actigraph could reliably determine the number of awakenings during the night unless it was a significant physical activity during the night. From the results of the work, it is not possible to say in general that all commercially available devices would be a reliable substitute for actigraphy.
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Advanced methods for sleep quality assessment
Doležalová, Anna ; Králík, Martin (referee) ; Ronzhina, Marina (advisor)
This diploma thesis is focused on advanced sleep assessment using deep learning. Metrics for sleep assessment and their use are described here. There are hearth rate and accelerometer data from Apple Watch used for classification. The basis for the classification was a model composed of 1D convolution networks in combination with recurrent neural network. LSTM and GRU were used as recurrent networks. Models were taught to classify into two, three and five phases. At last the resulting methods are compared.
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Identification of sleep disorders based on actigraphy data and sleep diaries
Molík, Miroslav ; Mekyska, Jiří (referee) ; Mikulec, Marek (advisor)
This master’s thesis deals with prediction of Parkinson's disease using sleep parameters from actigraphy and sleep diaries. The goal is to design a machine learning approach, which will be able to recognize pacients suffering from Parkinson's disease. For training dataset supplied by St. Anne's University Hospital Brno was used, which was variously modified for achieving best possible results. These adjustments were evaluated according to the results of the trained models and based on these results, two models (achieving test accuracies of 85 and 82%) were selected.
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