Apparatus and methods are provided for use with a subject who is undergoing respiration. A motion sensor senses motion of a subject. A breathing pattern analysis unit analyzes components of the sensed motion that result from the subject's respiration. The breathing pattern analysis unit includes double-movement-respiration-cycle-pattern-identification functionality that designates respiration cycles as being double-movement-respiration-cycles (DMRC's) by determining that the cycles define two subcycles. Double-movement-respiration-cycle-event-identification functionality of the breathing pattern analysis unit identifies a DMRC event by detecting that the subject has undergone a plurality of DMRC's. An output is generated that is indicative of the subject having used accessory muscles in breathing, in response to identification of the double-movement-respiration-cycle event. Other embodiments are also described.

Monitoring, predicting and treating clinical episodes

In the past decade, there has been a resurgence in the field of unobtrusive cardiomechanical assessment, through advancing methods for measuring and interpreting ballistocardiogram (BCG) and seismocardiogram (SCG) signals. Novel instrumentation solutions have enabled BCG and SCG measurement outside of clinical settings, in the home, in the field, and even in microgravity. Customized signal processing algorithms have led to reduced measurement noise, clinically relevant feature extraction, and signal modeling. Finally, human subjects physiology studies have been conducted using these novel instruments and signal processing tools with promising results. This paper reviews the recent advances in these areas of modern BCG and SCG research.

/pdf/ballistocardiography-and-seismocardiography-a-review-of-1qwmaxt19u.pdf

Ballistocardiography and Seismocardiography: A Review of Recent Advances

A method is provided for predicting an onset of an asthma attack. The method includes sensing at least one parameter of a subject without contacting or viewing the subject or clothes the subject is wearing, and predicting the onset of the asthma attack at least in part responsively to the sensed parameter. Also provided is a method for predicting an onset of an episode associated with congestive heart failure (CHF), including sensing at least one parameter of a subject without contacting or viewing the subject or clothes the subject is wearing, and predicting the onset of the episode at least in part responsively to the sensed parameter. Other embodiments are also described.

Techniques for prediction and monitoring of clinical episodes

A new indirect contact (IDC) electrocardiogram (ECG) measurement method (IDC-ECG) for monitoring ECG during sleep that is adequate for long-term use is provided The provided method did not require any direct conductive contact between the instrument and bare skin This method utilizes an array of high-input-impedance active electrodes fixed on the mattress and an indirect-skin-contact ground made of a large conductive textile sheet A thin cotton bedcover covered the mattress, electrodes, and conductive textile, and the participants were positioned on the mattress over the bedcover An ECG was successfully obtained, although the signal quality was lower and the motion artifact was larger than in conventional direct-contact measurements (DC-ECG) The results showed that further studies are required to apply the provided method to an ECG diagnosis of cardiovascular diseases However, currently the method can be used for HRV assessment with easy discrimination of R-peaks

ECG Recording on a Bed During Sleep Without Direct Skin-Contact

A ballistocardiograph records the mechanical activity of the heart. We present a novel algorithm for the detection of individual heart beats and beat-to-beat interval lengths in ballistocardiograms (BCGs) from healthy subjects. An automatic training step based on unsupervised learning techniques is used to extract the shape of a single heart beat from the BCG. Using the learned parameters, the occurrence of individual heart beats in the signal is detected. A final refinement step improves the accuracy of the estimated beat-to-beat interval lengths. Compared to many existing algorithms, the new approach offers heart rate estimates on a beat-to-beat basis. The agreement of the proposed algorithm with an ECG reference has been evaluated. A relative beat-to-beat interval error of 1.79% with a coverage of 95.94% was achieved on recordings from 16 subjects.

/pdf/adaptive-beat-to-beat-heart-rate-estimation-in-3lgxkxn7s5.pdf

Adaptive Beat-to-Beat Heart Rate Estimation in Ballistocardiograms

The cardio-respiratory signal is a fundamental vital sign used for assessment of a patient's status. Additionally, the cardio-respiratory signal provides a great deal of information to healthcare providers wishing to monitor healthy individuals. The air mattress sensor system allows the measurement of the respiration and heart beat movements without the use of a harness or sensor on the subject's body, which eliminates the difficulties these pose for long term measurements. In order to increase the sensitivity, a differential measurement technique between two air cells was used. The concept of a balancing tube between two air cells is suggested in order to increase the robustness against postural changes during the measurements. With this balancing tube, the meaningful frequency range could be selected using a pneumatic method. A mathematical model was constructed and validation experiments were performed for step and sinusoidal input signals. This technique was applied to measurements of respiration and heart beat movements in the supine posture on the bed, which showed potential for applications in sleep analysis, unconstrained healthcare monitoring and neonate monitoring.

https://iopscience.iop.org/article/10.1088/0967-3334/26/4/007/pdf

Air mattress sensor system with balancing tube for unconstrained measurement of respiration and heart beat movements

The imaging of neural sources of magnetoencephalographic data based on distributed source models requires additional constraints on the source distribution in order to overcome ill-posedness and obtain a plausible solution. The minimum l(p) norm (0 < p < or = 1) constraint is known to be appropriate for reconstructing focal sources distributed in several regions. A well-known recursive method for solving the l(p)-norm minimization problem, for example, is the focal underdetermined system solver (FOCUSS). However, this iterative algorithm tends to give spurious sources when the noise level is high. In this study, we present an algorithm to incorporate a smoothing technique into the FOCUSS algorithm and test different smoothing kernels in a surface-based cortical source space. Simulations with cortical source patches assumed in auditory areas show that the incorporation of the smoothing procedure improves the performance of the FOCUSS algorithm, and that using the geodesic distance for constructing a smoothing kernel is a better choice than using the Euclidean one, particularly when employing a cortical source space. We also apply these methods to a real data set obtained from an auditory experiment and illustrate their applicability to realistic data by presenting the reconstructed source images localized in the superior temporal gyrus.

Evaluation of smoothing in an iterative lp-norm minimization algorithm for surface-based source localization of MEG.

We present the method of the elimination of ECG artifact from the polysomnographic EEG and EGG. The idea of this method is that the ECG-corrupted EEG segment can be detected from ECG R-wave and regarded as a missing segment. After this, we used two interpolations to recover the missing segment. One is the Lagrange polynomial interpolation and the other is the least square error AR interpolation. We also compared the AR-method with the LMS adaptive noise canceling method. Simulations show the AR-method performs better than other methods. To apply to the real EEG and EOG signals practically, we also developed the algorithm to detect whether the artifact level is high or not. If the artifact level is high, then the interpolations are applied.

A study on the elimination of the ECG artifact in the polysomnographic EEG and EOG using AR model

We derived a generalized version of the regularized FOCUSS algorithm which was derived in a paper by Phillips, JW et al., (1997). It allows general forms of noise covariance and reduces depth effect when imaging focal neural sources from electroencephalography (EEG) / magnetoencephalography (MEG) data. We compared a depth-weighted regularized algorithm with FOCUSS and a regularized FOCUSS through simulation study. The suggested algorithm gave sparser and less spurious solutions than the others.

Regularized FOCUSS algorithm for EEG/MEG source imaging

The importance of the video monitoring during polysomnographic recording is increasing, but the analog-taped storage method currently in use has difficulties in random access and synchronization with the polysomnographic data. To overcome these limits we need to develop the digitalized integration and synchronization method. The digitization of the moving image during sleep requires at least the quality of detecting the relatively fast movement. It also requires the minimal volume of data for the storage and communication. We analyzed the two standards of digital moving image compression to find the optimal storing format for the video image during sleep. Based on this analysis, we developed the integration method using the format H.261, which has better performance than MPEG-1 in low bit rate of 128 kbps. We also used the QCIF format in H.261 to increase the frame rate, and added the time index to H.261 format to support random access. We synchronized these video images with the polysomnographic data.

http://ieeexplore.ieee.org/iel4/6018/16113/00746985.pdf

Jooman Han

Papers

Air mattress sensor system with balancing tube for unconstrained measurement of respiration and heart beat movements

Evaluation of smoothing in an iterative lp-norm minimization algorithm for surface-based source localization of MEG.

A study on the elimination of the ECG artifact in the polysomnographic EEG and EOG using AR model

Regularized FOCUSS algorithm for EEG/MEG source imaging

A study on the integration and synchronization of video image using H.261 in polysomnography