WO2009079977A2 - Procédé et dispositif pour le diagnostic préalable et l'accompagnement thérapeutique en médecine du sommeil - Google Patents

Procédé et dispositif pour le diagnostic préalable et l'accompagnement thérapeutique en médecine du sommeil Download PDF

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Publication number
WO2009079977A2
WO2009079977A2 PCT/DE2008/002034 DE2008002034W WO2009079977A2 WO 2009079977 A2 WO2009079977 A2 WO 2009079977A2 DE 2008002034 W DE2008002034 W DE 2008002034W WO 2009079977 A2 WO2009079977 A2 WO 2009079977A2
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WO
WIPO (PCT)
Prior art keywords
sleep
values
signals
stored
program
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/DE2008/002034
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German (de)
English (en)
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WO2009079977A3 (fr
WO2009079977A4 (fr
Inventor
Guy Leonard Kouemou
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Individual
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Individual
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Publication of WO2009079977A2 publication Critical patent/WO2009079977A2/fr
Publication of WO2009079977A3 publication Critical patent/WO2009079977A3/fr
Publication of WO2009079977A4 publication Critical patent/WO2009079977A4/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/003—Detecting lung or respiration noise
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4818—Sleep apnoea
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the invention relates to a method for the diagnosis of sleep disorders in the home user area, based on the analysis of sleep-related breathing noises of the patients.
  • One of the focal points for evaluating the quality of sleep is, in addition to the sleep-related history (sleep-related questionnaire), the detection and analysis of sleep stages. In many sleep disorders, the duration of sleep stages 3 and 4 (deep sleep) is reduced or the deep sleep phases are not present at all.
  • Electrophysiological Schlafpolygrafie that can be performed only in sleep laboratories of sleep medicine centers.
  • the patient is connected to a variety of sensors and examined for any existing sleep disorders.
  • the technical objective and the object of the invention is therefore to provide a practical and easy to use by any patient
  • the core of the invention consists in the fact that the evaluation of the quality of sleep by the software using artificial neural networks is performed and the evaluation is performed only on the basis of a breath sound recording.
  • the classification is based both on the evaluation of the root mean square squares over a certain signal duration, as well as on the evaluation of spectral components of the breath sound signal.
  • the breath sounds are recorded using an airborne microphone or a structure-borne sound microphone.
  • the airborne microphone is attached to a headband and attached to the patient's head (see Figure 2).
  • the Acoustic Microphone can also be mounted on a stand at a distance of 0-50cm from the patient's head (see Figure 3). For the use of the Köperschallmikrofons this is preferably attached to the upper lip (see Figure 4) or to the trachea.
  • breath sounds with a sample rate of, for example, up to 11.025 kHz and with e.g. 16 bit resolution sampled.
  • the data is compressed using a bandpass filter to a frequency range between 0 and 5500 Hz, since no higher frequencies are contained in the breath sounds.
  • a time-dependent and partial spectral analysis is performed. For each short-term spectrum, 256 samples were used by way of example (about 10 ms because of short-term stationarity of respiratory sounds). To accelerate the subsequent pattern recognition becomes a Data reduction by a factor of up to 16 using a filter bank approximation. The spectrum is divided into eg 16 areas. The mean values of the respective areas then form the filter bank coefficients, which are then combined to form a new feature vector. The filter bank analysis produces a compact approximation of the power density spectrum describing the signal as a sequence of feature vectors. Thus one obtains the spectral mean values of the breath sounds.
  • the analysis of the frequency banks showed that the four lower ones correlated better. For this reason, preferably the 4 lower frequency banks are included for sleep phase detection.
  • Example filter banks Bankl (0-124 Hz), Bank2 (124-264 Hz), Bank3 (264-421Hz), Bank4 (421- 597Hz).
  • the "root mean square" of the signal is calculated in the time domain.
  • the root mean square can be understood in German as the effective value of the signal.
  • the RMS of a signal x out of n sample points is calculated as follows:
  • the feature extraction consists of 2 stages. In the first stage, the data is normalized.
  • a 5 ⁇ N feature matrix is preferably calculated from the individual respiratory sounds.
  • N denotes the single vector from the segment duration a 30 seconds per vector (preferably). This corresponds, e.g. 2 points per minute, i. 120 points per hour, or 1200 points for a night recording of 10 hours.
  • Segmentation process The studies carried out in the present work were carried out against polysomnogramin derived sleep stages.
  • Respiratory noises of several patients during an entire night in an accredited sleep laboratory according to the recording method described above by means of a microphone system and a PC are recorded.
  • the recordings are performed for example with a self-written program.
  • the program is characterized by its high stability and robustness when handling large amounts of data (up to more than 650 MB over 10 hours recording here).
  • the program has some well-used analysis and signal processing functions that are very helpful for rapid viewing and analysis of the data.
  • the polysomnogram recorded in the sleep laboratory is normally evaluated automatically by the system analysis program the next day.
  • sleep phases and apnea appearances are calculated as a function of time by the system.
  • the responsible doctor later revised the automatic evaluations. Among other things, it checks the automatic determination of sleep stages carried out by the analysis program, and corrects them if necessary. A revision by the doctor can usually take between 45 minutes to over 2 hours to last; to endure, to continue. Since many patients in the sleep laboratory do not always have deep sleep phases due to sleep disturbances, it is important when examining the sleep phases to include both ill and healthy patients.
  • noise signals were processed in blocks of 30 seconds, since the sleep stages calculated from the polysomnogram used in the test were always determined over a period of 30 seconds.
  • a multilayer preferably forward-directed neural network is designed.
  • a training procedure with a corresponding algorithm (here preferably backpropagation) is defined.
  • network initialization function, segmented test and validation patterns, number of test cycles, termination criteria, and training duration should be entered.
  • the training of the neural network is started and finally a network is generated.
  • the trained network is then connected to the system in a modular way.
  • the system consists of a microphone system, a PC with AD-DA converter card (sound blaster card) and a monitoring tool.
  • Figure 5 shows an exemplary schematic representation of the processes in the software.
  • the recorded breath sound data is digitized.
  • the data is filtered and normalized in the signal preprocessing.
  • the feature extraction and handover of the data to the neural network or Hidden Markov Model Network occurs.
  • the network is trained with a teaching database containing recorded data from clinical sleep laboratories. These data are passed to another neural network or hidden Markov model network, where the detection of sleep disorders and sleep phase patterns takes place.
  • the network is trained with a further instruction database containing typical sleep disorder pictures from clinical sleep laboratories. If sleep disorders are detected, an advance diagnosis with therapeutic suggestions is made using a knowledge-based automaton.

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Veterinary Medicine (AREA)
  • Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Pulmonology (AREA)
  • Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

L'invention concerne un procédé et un dispositif pour le diagnostic préalable et l'accompagnement thérapeutique en médecine du sommeil. L'invention concerne notamment un procédé et un dispositif pour détecter les phases du sommeil, ces phases étant divisées en trois phases de sommeil, profond, léger et paradoxal, ou en deux phases du sommeil, profond et non profond, d'après une analyse des bruits respiratoires. Selon l'invention, les bruits respiratoires sont enregistrés au moyen d'un système de microphone et utilisés pour détecter les phases du sommeil par un procédé de type neuronal reposant sur une moyenne spectrale et une moyenne quadratique.
PCT/DE2008/002034 2007-12-21 2008-12-04 Procédé et dispositif pour le diagnostic préalable et l'accompagnement thérapeutique en médecine du sommeil Ceased WO2009079977A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102007063007A DE102007063007A1 (de) 2007-12-21 2007-12-21 Verfahren und Vorrichtung zur schlafmedizinschen Vorabdiagnose und Therapie-Begleitung
DE102007063007.9 2007-12-21

Publications (3)

Publication Number Publication Date
WO2009079977A2 true WO2009079977A2 (fr) 2009-07-02
WO2009079977A3 WO2009079977A3 (fr) 2010-04-01
WO2009079977A4 WO2009079977A4 (fr) 2010-05-20

Family

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Application Number Title Priority Date Filing Date
PCT/DE2008/002034 Ceased WO2009079977A2 (fr) 2007-12-21 2008-12-04 Procédé et dispositif pour le diagnostic préalable et l'accompagnement thérapeutique en médecine du sommeil

Country Status (2)

Country Link
DE (1) DE102007063007A1 (fr)
WO (1) WO2009079977A2 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111467644A (zh) * 2013-07-08 2020-07-31 瑞思迈传感器技术有限公司 用于睡眠管理的方法和系统
CN111887830A (zh) * 2020-09-10 2020-11-06 贵州省人民医院 睡眠监测方法、装置、设备及可读存储介质

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104622432B (zh) * 2015-02-06 2017-06-06 华南理工大学 基于低音比的睡眠鼾声监测方法及系统

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE4114357C2 (de) 1991-05-02 2000-07-06 Mueller & Sebastiani Elek Gmbh Vorrichtung zur automatischen Erkennung von Apnoe
IL122875A0 (en) 1998-01-08 1998-08-16 S L P Ltd An integrated sleep apnea screening system
US6666830B1 (en) * 2000-08-17 2003-12-23 East River Ventures, Lp System and method for detecting the onset of an obstructive sleep apnea event
ATE412366T1 (de) * 2000-12-29 2008-11-15 Ares Medical Inc Risikobewertung von schlafapnoe
JP4472294B2 (ja) * 2003-08-22 2010-06-02 株式会社サトー 睡眠時無呼吸症候群診断装置、並びに、信号解析装置及びその方法
CA2464029A1 (fr) * 2004-04-08 2005-10-08 Valery Telfort Moniteur d'aeration non invasif
DE102006017279A1 (de) * 2006-04-12 2007-10-18 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Automatische Detektion von Hypopnoen

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111467644A (zh) * 2013-07-08 2020-07-31 瑞思迈传感器技术有限公司 用于睡眠管理的方法和系统
CN111887830A (zh) * 2020-09-10 2020-11-06 贵州省人民医院 睡眠监测方法、装置、设备及可读存储介质

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Publication number Publication date
WO2009079977A3 (fr) 2010-04-01
DE102007063007A1 (de) 2009-06-25
WO2009079977A4 (fr) 2010-05-20

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