TY - CHAP A1 - Gaiduk, Maksym A1 - Kuhn, Ina A1 - Seepold, Ralf A1 - Ortega, Juan Antonio A1 - Martínez Madrid, Natividad T1 - A sensor grid for pressure and movement detection supporting sleep phase analysis T2 - Bioinformatics and Biomedical Engineering, 5th International Work-Conference (IWBBIO 2017), Granada, Spain, April 26–28, 2017, Proceedings, Part II, (Lecture Notes in Computer Science ; 10209) N2 - Sleep quality and in general, behavior in bed can be detected using a sleep state analysis. These results can help a subject to regulate sleep and recognize different sleeping disorders. In this work, a sensor grid for pressure and movement detection supporting sleep phase analysis is proposed. In comparison to the leading standard measuring system, which is Polysomnography (PSG), the system proposed in this project is a non-invasive sleep monitoring device. For continuous analysis or home use, the PSG or wearable Actigraphy devices tends to be uncomfortable. Besides this fact, they are also very expensive. The system represented in this work classifies respiration and body movement with only one type of sensor and also in a non-invasive way. The sensor used is a pressure sensor. This sensor is low cost and can be used for commercial proposes. The system was tested by carrying out an experiment that recorded the sleep process of a subject. These recordings showed the potential for classification of breathing rate and body movements. Although previous researches show the use of pressure sensors in recognizing posture and breathing, they have been mostly used by positioning the sensors between the mattress and bedsheet. This project however, shows an innovative way to position the sensors under the mattress. KW - Sensor grid KW - Movement detection KW - Sleep phase KW - Force resistor sensor Y1 - 2017 SN - 978-3-319-56154-7 SN - 978-3-319-56153-0 U6 - http://dx.doi.org/10.1007/978-3-319-56154-7_53 SP - 596 EP - 607 PB - Springer CY - Cham ER - TY - JOUR A1 - Gaiduk, Maksym A1 - Penzel, Thomas A1 - Ortega, Juan Antonio A1 - Seepold, Ralf T1 - Automatic sleep stages classification using respiratory, heart rate and movement signals JF - Physiological Measurement N2 - Objective: This paper presents an algorithm for non-invasive sleep stage identification using respiratory, heart rate and movement signals. The algorithm is part of a system suitable for long-term monitoring in a home environment, which should support experts analysing sleep. Approach: As there is a strong correlation between bio-vital signals and sleep stages, multinomial logistic regression was chosen for categorical distribution of sleep stages. Several derived parameters of three signals (respiratory, heart rate and movement) are input for the proposed method. Sleep recordings of five subjects were used for the training of a machine learning model and 30 overnight recordings collected from 30 individuals with about 27 000 epochs of 30 s intervals each were evaluated. Main results: The achieved rate of accuracy is 72% for Wake, NREM, REM (with Cohen's kappa value 0.67) and 58% for Wake, Light (N1 and N2), Deep (N3) and REM stages (Cohen's kappa is 0.50). Our approach has confirmed the potential of this method and disclosed several ways for its improvement. Significance: The results indicate that respiratory, heart rate and movement signals can be used for sleep studies with a reasonable level of accuracy. These inputs can be obtained in a non-invasive way applying it in a home environment. The proposed system introduces a convenient approach for a long-term monitoring system which could support sleep laboratories. The algorithm which was developed allows for an easy adjustment of input parameters that depend on available signals and for this reason could also be used with various hardware systems. KW - Sleep stage classification KW - Multinomial logistic regression KW - Non-invasive sleep study KW - Heart rate KW - Sleep study Y1 - 2018 U6 - http://dx.doi.org/10.1088/1361-6579/aaf5d4 SN - 0967-3334 VL - 39 IS - 12 ER - TY - CHAP A1 - Gaiduk, Maksym A1 - Seepold, Ralf A1 - Ortega, Juan Antonio A1 - Martínez Madrid, Natividad T1 - Comparison of sleep characteristics measurements BT - a case study with a population aged 65 and above T2 - 24rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2020), 16 - 18 September 2020, Verona, Italy, virtual; (Procedia Computer Science) N2 - Good sleep is crucial for a healthy life of every person. Unfortunately, its quality often decreases with aging. A common approach to measuring the sleep characteristics is based on interviews with the subjects or letting them fill in a daily questionnaire and afterward evaluating the obtained data. However, this method has time and personal costs for the interviewer and evaluator of responses. Therefore, it would be important to execute the collection and evaluation of sleep characteristics automatically. To do that, it is necessary to investigate the level of agreement between measurements performed in a traditional way using questionnaires and measurements obtained using electronic monitoring devices. The study presented in this manuscript performs this investigation, comparing such sleep characteristics as "time going to bed", "total time in bed", "total sleep time" and "sleep efficiency". A total number of 106 night records of elderly persons (aged 65+) were analyzed. The results achieved so far reveal the fact that the degree of agreement between the two measurement methods varies substantially for different characteristics, from 31 minutes of mean difference for "time going to bed" to 77 minutes for "total sleep time". For this reason, a direct exchange of objective and subjective measuring methods is currently not possible. KW - Sleep study KW - Sleep quality KW - PSQI KW - Sleep efficiency Y1 - 2020 U6 - http://dx.doi.org/10.1016/j.procs.2020.09.297 SN - 1877-0509 VL - 176 SP - 2341 EP - 2349 ER - TY - CHAP A1 - Gaiduk, Maksym A1 - Orcioni, Simone A1 - Conti, Massimo A1 - Seepold, Ralf A1 - Penzel, Thomas A1 - Martinez Madrid, Natividad A1 - Ortega, Juan Antonio T1 - Embedded system for non-obtrusive sleep apnea detection T2 - 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2020), 20-24 July 2020, Montreal, QC, Canada, virtual N2 - This document presents a new complete standalone system for a recognition of sleep apnea using signals from the pressure sensors placed under the mattress. The developed hardware part of the system is tuned to filter and to amplify the signal. Its software part performs more accurate signal filtering and identification of apnea events. The overall achieved accuracy of the recognition of apnea occurrence is 91%, with the average measured recognition delay of about 15 seconds, which confirms the suitability of the proposed method for future employment. The main aim of the presented approach is the support of the healthcare system with the cost-efficient tool for recognition of sleep apnea in the home environment. KW - Sleep apnea KW - Pressure sensors KW - Low-pass filters Y1 - 2020 SN - 978-1-7281-1990-8 U6 - http://dx.doi.org/10.1109/EMBC44109.2020.9176075 N1 - Volltextzugriff für Angehörige der Hochschule Konstanz via IEEE Xplore möglich SP - 2776 EP - 2779 ER - TY - JOUR A1 - Seepold, Ralf A1 - Gaiduk, Maksym A1 - Ortega, Juan Antonio A1 - Conti, Massimo A1 - Orcioni, Simone A1 - Martínez Madrid, Natividad T1 - Home hospital e-health centers for barrier-free and cross-border telemedicine JF - Intelligent Decision Technologies 2019, Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019) - Volume 1 ; (Smart Innovation, Systems and Technologies ; 143) N2 - The goal of the presented project is to develop the concept of home ehealth centers for barrier-free and cross-border telemedicine. AAL technologies are already present on the market but there is still a gap to close until they can be used for ordinary patient needs. The general idea needs to be accompanied by new services, which should be brought together in order to provide a full coverage of service for the users. Sleep and stress were chosen as predominant diseases for a detailed study within this project because of their widespread influence in the population. The executed scientific study of available home devices analyzing sleep has provided the necessary to select appropriate devices. The first choice for the project implementation is the device EMFIT QS+. This equipment provides a part of a complete system that a home telemedical hospital can provide at a level of precision and communication with internal and/or external health services. KW - AAL KW - E-Health KW - Telemedicine Y1 - 2019 UR - https://doi.org/10.1007/978-981-13-8303-8_28 SN - 978-981-13-8302-1 SN - 978-981-13-8303-8 SP - 307 EP - 316 PB - Springer CY - Singapore ET - First online: 2 June 2019 ER - TY - JOUR A1 - Gaiduk, Maksym A1 - Seepold, Ralf A1 - Martínez Madrid, Natividad A1 - Ortega, Juan Antonio A1 - Penzel, Thomas T1 - Non-invasives System für die kontinuierliche Schlafanalyse JF - 6. Ambient Medicine® Forum „Assistive Technik für selbstbestimmtes Wohnen“, 19.-20. Februar 2019, Hochschule Kempten, Tagungsband N2 - Die Erholung unseres Körpers und Gehirns von Müdigkeit ist direkt abhängig von der Qualität des Schlafes, die aus den Ergebnissen einer Schlafstudie ermittelt werden kann. Die Klassifizierung der Schlafstadien ist der erste Schritt dieser Studie und beinhaltet die Messung von Biovitaldaten und deren weitere Verarbeitung. Das non-invasive Schlafanalyse-System basiert auf einem Hardware-Sensornetz aus 24 Drucksensoren, das die Schlafphasenerkennung ermöglicht. Die Drucksensoren sind mit einem energieeffizienten Mikrocontroller über einen systemweiten Bus mit Adressarbitrierung verbunden. Ein wesentlicher Unterschied dieses Systems im Vergleich zu anderen Ansätzen ist die innovative Art, die Sensoren unter der Matratze zu platzieren. Diese Eigenschaft erleichtert die kontinuierliche Nutzung des Systems ohne fühlbaren Einfluss auf das gewohnte Bett. Das System wurde getestet, indem Experimente durchgeführt wurden, die den Schlaf verschiedener gesunder junger Personen aufzeichneten. Die ersten Ergebnisse weisen auf das Potenzial hin, nicht nur Atemfrequenz und Körperbewegung, sondern auch Herzfrequenz zu erfassen. KW - Schlafanalyse KW - Sleep study KW - Atmung Y1 - 2019 SN - 978-3-7369-9961-9 SP - 23 EP - 28 PB - Cuvillier Verlag CY - Göttingen ER - TY - CHAP A1 - Gaiduk, Maksym A1 - Wehrle, Dennis A1 - Seepold, Ralf A1 - Ortega, Juan Antonio T1 - Non-obtrusive system for overnight respiration and heartbeat tracking T2 - 24rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2020), 16 - 18 September 2020, Verona, Italy, virtual; (Procedia Computer Science) N2 - Polysomnography is a gold standard for a sleep study, and it provides very accurate results, but its cost (both personnel and material) are quite high. Therefore, the development of a low-cost system for overnight breathing and heartbeat monitoring, which provides more comfort while recording the data, is a well-motivated challenge. The system proposed in this manuscript is based on the usage of resistive pressure sensors installed under the mattress. These sensors can measure slight pressure changes provoked during breathing and heartbeat. The captured signal requires advanced processing, like applying filters and amplifiers before the analog signal is ready for the next step. Then, the output signal is digitalized and further processed by an algorithm that performs a custom filtering before it can recognize breathing and heart rate in real-time. The result can be directly visualized. Furthermore, a CSV file is created containing the raw data, timestamps, and unique IDs to facilitate further processing. The achieved results are promising, and the average deviation from a reference device is about 4bpm. KW - Biosignal analysis KW - Sleep medicine KW - Heart rate KW - Breathing Y1 - 2020 U6 - http://dx.doi.org/10.1016/j.procs.2020.09.282 SN - 1877-0509 VL - 176 SP - 2746 EP - 2755 ER - TY - CHAP A1 - Gaiduk, Maksym A1 - Seepold, Ralf A1 - Penzel, Thomas A1 - Ortega, Juan Antonio A1 - Glos, Martin A1 - Martínez Madrid, Natividad T1 - Recognition of sleep/wake states analyzing heart rate, breathing and movement signals T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2019), 23-27 July 2019, Messe Berlin, Germany N2 - This document presents an algorithm for a non-obtrusive recognition of Sleep/Wake states using signals derived from ECG, respiration, and body movement captured while lying in a bed. As a core mathematical base of system data analytics, multinomial logistic regression techniques were chosen. Derived parameters of the three signals are used as the input for the proposed method. The overall achieved accuracy rate is 84% for Wake/Sleep stages, with Cohen’s kappa value 0.46. The presented algorithm should support experts in analyzing sleep quality in more detail. The results confirm the potential of this method and disclose several ways for its improvement. KW - Sleep study KW - Multinomial logistic regression KW - Sleep/Wake states Y1 - 2019 SN - 978-1-5386-1311-5 U6 - http://dx.doi.org/10.1109/EMBC.2019.8857596 N1 - Volltextzugriff für Angehörige der Hochschule Konstanz via IEEE Xplore möglich SP - 5712 EP - 5715 PB - IEEE ER - TY - CHAP A1 - Gaiduk, Maksym A1 - Vunderl, Bruno A1 - Seepold, Ralf A1 - Ortega, Juan Antonio A1 - Penzel, Thomas T1 - Sensor-Mesh-Based System with Application on Sleep Study T2 - Bioinformatics and Biomedical Engineering, 6th International Work-Conference (IWBBIO 2018), 25th-27th April 2018, Granada, Spain - (Lecture Notes in Computer Science ; Vol. 10814) N2 - The process of restoring our body and brain from fatigue is directly depend-ing on the quality of sleep. It can be determined from the report of the sleep study results. Classification of sleep stages is the first step of this study and this includes the measurement of biovital data and its further processing. In this work, the sleep analysis system is based on a hardware sensor net, namely a grid of 24 pressure sensors, supporting sleep phase recognition. In comparison to the leading standard, which is polysomnography, the proposed approach is a non-invasive system. It recognises respiration and body move-ment with only one type of low-cost pressure sensors forming a mesh archi-tecture. The nodes implement as a series of pressure sensors connected to a low-power and performant microcontroller. All nodes are connected via a system wide bus with address arbitration. The embedded processor is the mesh network endpoint that enables network configuration, storing and pre-processing of the data, external data access and visualization. The system was tested by executing experiments recording the sleep of different healthy young subjects. The results obtained have indicated the po-tential to detect breathing rate and body movement. A major difference of this system in comparison to other approaches is the innovative way to place the sensors under the mattress. This characteristic facilitates the continuous using of the system without any influence on the common sleep process. KW - Movement detection KW - Respiration rate KW - Sleep study KW - FSR sensor Y1 - 2018 SN - 978-3-319-78759-6 SN - 978-3-319-78758-9 U6 - http://dx.doi.org/10.1007/978-3-319-78759-6_34 SP - 371 EP - 382 PB - Springer CY - Cham ER -