Abstract: A method and system for evaluating a subject's weight calculates a moving average of the weight of the subject based on several consecutive weight measurements e.g. on consecutive days. Rather than looking for absolute weight gains of e.g. 2 kg in 48 hours, as with currently accepted guidelines, each new measurement is compared with the calculated moving average, and a first form of alert is generated if the new measurement differs from the calculated moving average by more than a with expected range threshold amount such as by one standard deviation. If the first form of alert is generated consecutively on a predetermined number of successive iterations, a second form of alert is generated different from the first form of alert. The system and method does not assume a stable underlying patient weight, and absolute weight gains over e.g.

Abstract: An apparatus for diagnosing sleep apnea only uses an electrocardiogram signal and a computer processor and associated computer code that are configured to analyze the electrocardiogram signal and classify each time period of the electrocardiogram signal as either apneic or normal. A diagnostic measure of sleep apnea for the human patient is provided based on classification results obtained by combining a results for various electrocardiogram signal time periods. A computer-readable medium has computer code that causes the computer to analyze an electrocardiogram signal relating to the human patient and classify each time period in a set of time periods as either apneic or normal. A diagnostic measure of sleep apnea is provided based on classification results obtained by combining a plurality of results from the set of time periods using either time or frequency domain processing, or both.

Abstract: An apparatus, system, and method for monitoring a person suffering from a chronic medical condition predicts and assesses physiological changes which could affect the care of that subject. Examples of such chronic diseases include (but are not limited to) heart failure, chronic obstructive pulmonary disease, asthma, and diabetes. Monitoring includes measurements of respiratory movements, which can then be analyzed for evidence of changes in respiratory rate, or for events such as hypoponeas, apneas and periodic breathing. Monitoring may be augmented by the measurement of nocturnal heart rate in conjunction with respiratory monitoring. Additional physiological measurements can also be taken such as subjective symptom data, blood pressure, blood oxygen levels, and various molecular markers. Embodiments for detection of respiratory patterns and heart rate are disclosed, together with exemplar implementations of decision processes based on these measurements.

Abstract: An apparatus, system, and method is disclosed for monitoring the motion, breathing, heart rate of humans in a convenient and low-cost fashion, and for deriving and displaying useful measurements of cardiorespiratory performance from the measured signals. The motion, breathing, and heart rate signals are obtained through a processing applied to a raw signal obtained in a non-contact fashion, typically using a radio-frequency sensor. Processing into separate cardiac and respiratory components is described. The heart rate can be determined by using either spectral or time-domain processing. The respiratory rate can be calculated using spectral analysis. Processing to derive the heart rate, respiratory sinus arrhythmia, or a ventilatory threshold parameter using the system is described. The sensor, processing, and display can be incorporated in a single device which can be worn or held close to the body while exercising (e.g.

Abstract: An apparatus for diagnosing sleep apnea only uses an electrocardiogram signal and a computer processor and associated computer code that are configured to analyze the electrocardiogram signal and classify each time period of the electrocardiogram signal as either apneic or normal. A diagnostic measure of sleep apnea for the human patient is provided based on classification results obtained by combining a results for various electrocardiogram signal time periods. A computer-readable medium has computer code that causes the computer to analyze an electrocardiogram signal relating to the human patient and classify each time period in a set of time periods as either apneic or normal. A diagnostic measure of sleep apnea is provided based on classification results obtained by combining a plurality of results from the set of time periods using either time or frequency domain processing, or both.

Abstract: There is provided a method of determining a diagnostic measure of sleep apnea including the following steps: acquiring an electrocardiogram signal, calculating a set of RR intervals and electrocardiogram-derived respiratory signal from said electrocardiogram, and hence calculating a set of spectral and time-domain measurements over time periods including power spectral density, mean, and standard deviation. These measurements are processed by a classifier model which has been trained on a pre-existing data base of electrocardiogram signals to provide a probability of a specific time period containing apneic episodes or otherwise. These probabilities can be combined to form an overall diagnostic measure. The system also provides a system and apparatus for providing a diagnostic measure of sleep apnea.