SeisMote: A Multi-Sensor Wireless Platform for Cardiovascular Monitoring in Laboratory, Daily Life, and Telemedicine

This article presents a new wearable platform, SeisMote, for the monitoring of cardiovascular function in controlled conditions and daily life. It consists of a wireless network of sensorized nodes providing simultaneous multiple measures of electrocardiogram (ECG), acceleration, rotational velocity, and photoplethysmogram (PPG) from different body areas. A custom low-power transmission protocol was developed to allow the concomitant real-time monitoring of 32 signals (16 bit @200 Hz) from up to 12 nodes with a jitter in the among-node time synchronization lower than 0.2 ms. The BluetoothLE protocol may be used when only a single node is needed. Data can also be collected in the off-line mode. Seismocardiogram and pulse transit times can be derived from the collected data to obtain additional information on cardiac mechanics and vascular characteristics. The employment of the system in the field showed recordings without data gaps caused by transmission errors, and the duration of each battery charge exceeded 16 h. The system is currently used to investigate strategies of hemodynamic regulation in different vascular districts (through a multisite assessment of ECG and PPG) and to study the propagation of precordial vibrations along the thorax. The single-node version is presently exploited to monitor cardiac patients during telerehabilitation.

The intensity of oscillations of the photoplethysmographic waveform variability at frequencies 0.04-0.4 Hz is effective marker of hypertension and coronary artery disease in males

Background: It is believed that the intensity of oscillations in the photoplethysmographic waveform variability reflects the activity of vascular regulatory mechanisms. However, the relationship of such fluctuations with the state of health is poorly understood.Purpose: The aim of our study was to assess the possibility of using spectral indices that reflect the intensity of oscillations of the photoplethysmographic waveform variability at frequencies 0.04-0.4 Hz as markers of hypertension and coronary artery disease. We did not study women to exclude the influence of menopause and sex hormones on the results.Materials and Methods: We compared synchronous 10-minute records of finger photoplethysmogram and respiration at rest in 30 healthy males (48.8 ± 4.5 years; data presented as Mean ± SD) versus 30 patients with hypertension (aged 49.0 ± 4.3 years) versus 30 patients with stable coronary artery disease (49.2 ± 4.8 years). Percentages of high-frequency and low-frequency ranges in the total power of photoplethysmographic waveform variability spectrum (HF% and LF%), and LF/HF ratio were assessed.Results: HF% are subject to by 2- to 5-fold increase in hypertensive patients (p < .001) and up to an 8-fold increase in patients with coronary artery disease (p < .001) when compared with healthy persons. On the contrary, LF% is reduced by 1.5-5 times in all patients when compared with healthy people (p < .001). We identified cut-off points for each photoplethysmographic index to distinguish patients with coronary artery disease or hypertension from healthy subjects. Multiple logistic regression models based on photoplethysmographic waveform variability indices had sufficient sensitivity and specificity for patients with hypertension or coronary artery disease.Conclusion: Frequency-domain indices of photoplethysmographic waveform variability (in particular, HF%, LF%, and LF/HF) are sufficiently sensitive and specific markers of hypertension and coronary artery disease in adult males.

Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network

Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using just a photoplethysmogram (PPG) to estimate BP is a potential solution investigated in our study. We analyzed the MIMIC III database for high-quality PPG and arterial BP waveforms, resulting in over 700 h of signals after preprocessing, belonging to 510 subjects. We then used the PPG alongside its first and second derivative as inputs into a novel spectro-temporal deep neural network with residual connections. We have shown in a leave-one-subject-out experiment that the network is able to model the dependency between PPG and BP, achieving mean absolute errors of 9.43 for systolic and 6.88 for diastolic BP. Additionally we have shown that personalization of models is important and substantially improves the results, while deriving a good general predictive model is difficult. We have made crucial parts of our study, especially the list of used subjects and our neural network code, publicly available, in an effort to provide a solid baseline and simplify potential comparison between future studies on an explicit MIMIC III subset.

Unobtrusive Estimation of Cardiovascular Parameters with Limb Ballistocardiography

This study investigates the potential of the limb ballistocardiogram (BCG) for unobtrusive estimation of cardiovascular (CV) parameters. In conjunction with the reference CV parameters (including diastolic, pulse, and systolic pressures, stroke volume, cardiac output, and total peripheral resistance), an upper-limb BCG based on an accelerometer embedded in a wearable armband and a lower-limb BCG based on a strain gauge embedded in a weighing scale were instrumented simultaneously with a finger photoplethysmogram (PPG). To standardize the analysis, the more convenient yet unconventional armband BCG was transformed into the more conventional weighing scale BCG (called the synthetic weighing scale BCG) using a signal processing procedure. The characteristic features were extracted from these BCG and PPG waveforms in the form of wave-to-wave time intervals, wave amplitudes, and wave-to-wave amplitudes. Then, the relationship between the characteristic features associated with (i) the weighing scale BCG-PPG pair and (ii) the synthetic weighing scale BCG-PPG pair versus the CV parameters, was analyzed using the multivariate linear regression analysis. The results indicated that each of the CV parameters of interest may be accurately estimated by a combination of as few as two characteristic features in the upper-limb or lower-limb BCG, and also that the characteristic features recruited for the CV parameters were to a large extent relevant according to the physiological mechanism underlying the BCG.

A New Wearable Device for Blood Pressure Estimation Using Photoplethysmogram

We present a novel smartwatch, CareUp ® , for estimating the Blood Pressure (BP) in real time. It consists of two pulse oximeters: one placed on the back and one on the front of the device. Placing the index finger on the front oximeter starts the acquisition of two photoplethysmograms (PPG); the signals are then filtered and cross-correlated to obtain a Time Delay between them, called Pulse Transit Time (PTT). The Heart Rate (HR) (estimated from the finger PPG) and the PTT are then input in a linear model to give an estimation of the Systolic and Diastolic BP. The performance of the smartwatch in measuring BP have been validated in the Institut Coeur Paris Centre Turin (ICPC), using a sphygmomanometer, on 44 subjects. During the validation, the measures of the CareUp ® were compared to those of two oscillometry-based devices already available on the market: Thuasne ® and Magnien ® . The results showed an accuracy comparable to the oscillometry-based devices and they almost agreed with the American Association for the Advancement of Medical Instrumentation standard for non-automated sphygmomanometers. The integration of the BP estimation algorithm in the smartwatch makes the CareUp ® an easy-to-use, wearable device for monitoring the BP in real time.

A Chair-Based Unconstrained/Nonintrusive Cuffless Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram

Hypertension is a well-known chronic disease that causes complications such as cardiovascular diseases or stroke, and thus needs to be continuously managed by using a simple system for measuring blood pressure. The existing method for measuring blood pressure uses a wrapping cuff, which makes measuring difficult for patients. To address this problem, cuffless blood pressure measurement methods that detect the peak pressure via signals measured using photoplethysmogram (PPG) and electrocardiogram (ECG) sensors and use it to calculate the pulse transit time (PTT) or pulse wave velocity (PWV) have been studied. However, a drawback of these methods is that a user must be able to recognize and establish contact with the sensor. Furthermore, the peak of the PPG or ECG cannot be detected if the signal quality drops, leading to a decrease in accuracy. In this study, a chair-type system that can monitor blood pressure using polyvinylidene fluoride (PVDF) films in a nonintrusive manner to users was developed. The proposed method also uses instantaneous phase difference (IPD) instead of PTT as the feature value for estimating blood pressure. Experiments were conducted using a blood pressure estimation model created via an artificial neural network (ANN), which showed that IPD could estimate more accurate readings of blood pressure compared to PTT, thus demonstrating the possibility of a nonintrusive blood pressure monitoring system.

In-Home Cardiovascular Monitoring System for Heart Failure: Comparative Study

Background

There is a pressing need to reduce the hospitalization rate of heart failure patients to limit rising health care costs and improve outcomes. Tracking physiologic changes to detect early deterioration in the home has the potential to reduce hospitalization rates through early intervention. However, classical approaches to in-home monitoring have had limited success, with patient adherence cited as a major barrier. This work presents a toilet seat–based cardiovascular monitoring system that has the potential to address low patient adherence as it does not require any change in habit or behavior.

Objective

The objective of this work was to demonstrate that a toilet seat–based cardiovascular monitoring system with an integrated electrocardiogram, ballistocardiogram, and photoplethysmogram is capable of clinical-grade measurements of systolic and diastolic blood pressure, stroke volume, and peripheral blood oxygenation.

Methods

The toilet seat–based estimates of blood pressure and peripheral blood oxygenation were compared to a hospital-grade vital signs monitor for 18 subjects over an 8-week period. The estimated stroke volume was validated on 38 normative subjects and 111 subjects undergoing a standard echocardiogram at a hospital clinic for any underlying condition, including heart failure.

Results

Clinical grade accuracy was achieved for all of the seat measurements when compared to their respective gold standards. The accuracy of diastolic blood pressure and systolic blood pressure is 1.2 (SD 6.0) mm Hg (N=112) and –2.7 (SD 6.6) mm Hg (N=89), respectively. Stroke volume has an accuracy of –2.5 (SD 15.5) mL (N=149) compared to an echocardiogram gold standard. Peripheral blood oxygenation had an RMS error of 2.3% (N=91).

Conclusions

A toilet seat–based cardiovascular monitoring system has been successfully demonstrated with blood pressure, stroke volume, and blood oxygenation accuracy consistent with gold standard measures. This system will be uniquely positioned to capture trend data in the home that has been previously unattainable. Demonstration of the clinical benefit of the technology requires additional algorithm development and future clinical trials, including those targeting a reduction in heart failure hospitalizations.

Continuous Vital Sign Analysis to Predict Secondary Neurological Decline After Traumatic Brain Injury

Background: In the acute resuscitation period after traumatic brain injury (TBI), one of the goals is to identify those at risk for secondary neurological decline (ND), represented by a constellation of clinical signs that can be identified as objective events related to secondary brain injury and independently impact outcome. We investigated whether continuous vital sign variability and waveform analysis of the electrocardiogram (ECG) or photoplethysmogram (PPG) within the first hour of resuscitation may enhance the ability to predict ND in the initial 48 hours after traumatic brain injury (TBI).

Methods: Retrospective analysis of ND in TBI patients enrolled in the prospective Oximetry and Noninvasive Predictors Of Intervention Need after Trauma (ONPOINT) study. ND was defined as any of the following occurring in the first 48 h: new asymmetric pupillary dilatation (>2 mm), 2 point GCS decline, interval worsening of CT scan as assessed by the Marshall score, or intervention for cerebral edema. Beat-to-beat variation of ECG or PPG, as well as waveform features during the first 15 and 60 min after arrival in the TRU were analyzed to determine physiologic parameters associated with future ND. Physiologic and admission clinical variables were combined in multivariable logistic regression models predicting ND and inpatient mortality.

Results: There were 33 (17%) patients with ND among 191 patients (mean age 43 years old, GCS 13, ISS 12, 69% men) who met study criteria. ND was associated with ICU admission (P < 0.001) and inpatient mortality (P < 0.001). Both ECG (AUROC: 0.84, 95% CI: 0.76,0.93) and PPG (AUROC: 0.87, 95% CI: 0.80, 0.93) analyses during the first 15 min of resuscitation demonstrated a greater ability to predict ND then clinical characteristics alone (AUROC: 0.69, 95% CI: 0.59, 0.8). Age (P = 0.02), Marshall score (P = 0.001), penetrating injury (P = 0.02), and predictive probability for ND by PPG analysis at 15 min (P = 0.03) were independently associated with inpatient mortality.

Conclusions: Analysis of variability and ECG or PPG waveform in the first minutes of resuscitation may represent a non-invasive early marker of future ND.

On the Feasibility of Low-Cost Wearable Sensors for Multi-Modal Biometric Verification

Biometric systems designed on wearable technology have substantial differences from traditional biometric systems. Due to their wearable nature, they generally capture noisier signals and can only be trained with signals belonging to the device user (biometric verification). In this article, we assess the feasibility of using low-cost wearable sensors—photoplethysmogram (PPG), electrocardiogram (ECG), accelerometer (ACC), and galvanic skin response (GSR)—for biometric verification. We present a prototype, built with low-cost wearable sensors, that was used to capture data from 25 subjects while seated (at resting state), walking, and seated (after a gentle stroll). We used this data to evaluate how the different combinations of signals affected the biometric verification process. Our results showed that the low-cost sensors currently being embedded in many fitness bands and smart-watches can be combined to enable biometric verification. We report and compare the results obtained by all tested configurations. Our best configuration, which uses ECG, PPG and GSR, obtained 0.99 area under the curve and 0.02 equal error rate with only 60 s of training data. We have made our dataset public so that our work can be compared with proposals developed by other researchers.

Extracting Instantaneous Respiratory Rate From Multiple Photoplethysmogram Respiratory-Induced Variations

In this study, we proposed a novel method for extracting the instantaneous respiratory rate (IRR) from the pulse oximeter photoplethysmogram (PPG). The method was performed in three main steps: (1) a time-frequency transform called synchrosqueezing transform (SST) was used to extract the respiratory-induced intensity, amplitude and frequency variation signals from PPG, (2) the second SST was applied to each extracted respiratory-induced variation signal to estimate the corresponding IRR, and (3) the proposed peak-conditioned fusion method then combined the IRR estimates to calculate the final IRR. We validated the implemented method with capnography and nasal/oral airflow as the reference RR using the limits of agreement (LOA) approach. Compared to simple fusion and single respiratory-induced variation estimations, peak-conditioned fusion shows better performance. It provided a bias of 0.28 bpm with the 95% LOAs ranging from −3.62 to 4.17, validated against capnography and a bias of 0.04 bpm with the 95% LOAs ranging from −5.74 to 5.82, validated against nasal/oral airflow. This algorithm would expand the functionality of a conventional pulse oximetry beyond the measurement of heart rate and oxygen saturation to measure the respiratory rate continuously and instantly.

Assessing mental stress from the photoplethysmogram: a numerical study

OBJECTIVE

Mental stress is detrimental to cardiovascular health, being a risk factor for coronary heart disease and a trigger for cardiac events. However, it is not currently routinely assessed. The aim of this study was to identify features of the photoplethysmogram (PPG) pulse wave which are indicative of mental stress.

APPROACH

A numerical model of pulse wave propagation was used to simulate blood pressure signals, from which simulated PPG pulse waves were estimated using a transfer function. Pulse waves were simulated at six levels of stress by changing the model input parameters both simultaneously and individually, in accordance with haemodynamic changes associated with stress. Thirty-two feature measurements were extracted from pulse waves at three measurement sites: the brachial, radial and temporal arteries. Features which changed significantly with stress were identified using the Mann-Kendall monotonic trend test.

MAIN RESULTS

Seventeen features exhibited significant trends with stress in measurements from at least one site. Three features showed significant trends at all three sites: the time from pulse onset to peak, the time from the dicrotic notch to pulse end, and the pulse rate. More features showed significant trends at the radial artery (15) than the brachial (8) or temporal (7) arteries. Most features were influenced by multiple input parameters.

SIGNIFICANCE

The features identified in this study could be used to monitor stress in healthcare and consumer devices. Measurements at the radial artery may provide superior performance than the brachial or temporal arteries. In vivo studies are required to confirm these observations.

Profiling the propagation of error from PPG to HRV features in a wearable physiological-monitoring device

Wearable physiological monitors are becoming increasingly commonplace in the consumer domain, but in literature there exists no substantive studies of their performance when measuring the physiology of ambulatory patients. In this Letter, the authors investigate the reliability of the heart-rate (HR) sensor in an exemplar ‘wearable’ wrist-worn monitoring system (the Microsoft Band 2); their experiments quantify the propagation of error from (i) the photoplethysmogram (PPG) acquired by pulse oximetry, to (ii) estimation of HR, and (iii) subsequent calculation of HR variability (HRV) features. Their experiments confirm that motion artefacts account for the majority of this error, and show that the unreliable portions of HR data can be removed, using the accelerometer sensor from the wearable device. The experiments further show that acquired signals contain noise with substantial energy in the high-frequency band, and that this contributes to subsequent variability in standard HRV features often used in clinical practice. The authors finally show that the conventional use of long-duration windows of data is not needed to perform accurate estimation of time-domain HRV features.

Robust Sensing of Distal Pulse Waveforms on a Modified Weighing Scale for Ubiquitous Pulse Transit Time Measurement

The measurement of aortic pulse transit time (PTT), the time for the arterial pulse wave to travel from the carotid to the femoral artery, can provide valuable insight into cardiovascular health, specifically regarding arterial stiffness and blood pressure (BP). To measure aortic PTT, both proximal and distal arterial pulse timings are required. Recently, our group has demonstrated that the ballistocardiogram signal measured on a modified weighing scale can provide an unobtrusive, yet accurate, means of obtaining a proximal timing reference; however, there are no convenient, reliable methods to extract the distal timing from a subject standing on the modified weighing scale. It is common to use a photoplethysmograph (PPG) attached to a toe to measure this distal pulse, but we discovered that this signal is greatly deteriorated as the subject stands on the scale. In this paper, we propose a novel method to measure the distal pulse using a custom reflective PPG array attached to the dorsum side of the foot (D-PPG). A total of 12 subjects of varying skin tones were recruited to assess the preliminary validation of this approach. Pulse measurements using the D-PPG were taken from seated and standing subjects, and the commercially available PPG were measured for facilitating comparison of timing measurements. We show that the D-PPG was the only sensor to retain the high detection rate of feasible timing values. To further test and optimize the system, various factors such as applied pressure, measurement location, and LED/photodiode configuration were tested.

Simultaneous Monitoring of Ballistocardiogram and Photoplethysmogram Using a Camera

We present a noncontact method to measure ballistocardiogram (BCG) and photoplethysmogram (PPG) simultaneously using a single camera. The method tracks the motion of facial features to determine displacement BCG, and extracts the corresponding velocity and acceleration BCGs by taking first and second temporal derivatives from the displacement BCG, respectively. The measured BCG waveforms are consistent with those reported in the literature and also with those recorded with an accelerometer-based reference method. The method also tracks PPG based on the reflected light from the same facial region, which makes it possible to track both BCG and PPG with the same optics. We verify the robustness and reproducibility of the noncontact method with a small pilot study with 23 subjects. The presented method is the first demonstration of simultaneous BCG and PPG monitoring without wearing any extra equipment or marker by the subject.

Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering

So far, many approaches have been developed for motion artifact (MA) reduction from photoplethysmogram (PPG). Specifically, single-input MA reduction methods are useful to apply wearable and mobile healthcare systems because of their low hardware costs and simplicity. However, most of them are insufficiently developed to be used in real-world situations, and they suffer from a phase distortion problem. In this study, we propose a novel single-input MA reduction algorithm based on time-variant forward-backward harmonic notch filtering. To verify the proposed method, we collected real PPG data corrupted by MA and compared it with existing single-input MA reduction methods. In conclusion, the proposed zero-phase line enhancer (ZLE) was found to be superior for MA reduction and exhibited zero phase response.

Correlations Between Cardiovascular Autonomic Control Indices During the Two-hour Immobilization Test in Healthy Subjects

Objective:

The aim of the present study was to assess the features of dynamics of cardiovascular autonomic indices and correlations between them during the two-hour immobilization test in healthy subjects.

Methods:

Photoplethysmogram (PPG) and electrocardiogram were recorded simultaneously during the two-hour immobilization test in 14 healthy subjects (5 men and 9 women) aged 29±5 years (mean±SD). Dynamics of heart rate variability (HRV) power spectrum in high-frequency and low-frequency ranges (in ms2 and percents of total spectral power), mean heart rate (HR), and index S of synchronization between 0.1-Hz rhythms in PPG and HR were analyzed.

Results:

Individual dynamics of all studied cardiovascular autonomic indices during the two-hour immobilization test was unique in each healthy subject. Two groups of healthy subjects were identified basing on individual features of autonomic control. The group with initial low level of index S maintained the low level of S during the two-hour immobilization test. The group with initial high index S maintained the high level of S only during the first 100 minutes of test. During the last 20 minutes of test, index S was similar in both groups. Many cardiovascular autonomic indices correlate between themselves for an individual subject, but they do not correlate between the subjects. Multiple regression analysis in each subject has shown a high correlation between mean HR and all other studied autonomic parameters in 57% of subjects (multiple R>0.9, P<0.05). For 204 records analyzed without taking into account the individual features of subjects, the above mentioned correlation was smaller (multiple R=0.45, P<0.001). Index S was found out to be the most independent one among the autonomic indices.

Conclusion:

Cardiovascular autonomic control is characterized by a pronounced variability among healthy subjects and stability in time in each subject. We have not found any regularity in variation of cardiovascular autonomic indices, which is common for the entire group of healthy subjects during the two-hour immobilization test. Mean HR is a summary index of efficiency of heart autonomic control. Index S is the most independent cardiovascular autonomic parameter.

Low-Power Photoplethysmogram Acquisition Integrated Circuit with Robust Light Interference Compensation

To overcome light interference, including a large DC offset and ambient light variation, a robust photoplethysmogram (PPG) readout chip is fabricated using a 0.13-μm complementary metal–oxide–semiconductor (CMOS) process. Against the large DC offset, a saturation detection and current feedback circuit is proposed to compensate for an offset current of up to 30 μA. For robustness against optical path variation, an automatic emitted light compensation method is adopted. To prevent ambient light interference, an alternating sampling and charge redistribution technique is also proposed. In the proposed technique, no additional power is consumed, and only three differential switches and one capacitor are required. The PPG readout channel consumes 26.4 μW and has an input referred current noise of 260 pArms.

Effect of patient positioning on toe pressure measurement using noninvasive vascular testing

OBJECTIVE

Interpretation of digital pressure in the assessment of healing potential for diabetic foot disease has become common because of the potential for false elevation with the ankle-brachial index. However, the specific testing protocol for segmental Doppler examinations and photoplethysmography require patients to be in the supine position, with the lower limbs at heart level, in order to minimise the effect of hydrostatic pressure. This may be difficult in many patients with lower extremity pathology, particularly those who are nonambulatory, with painful wounds, or with orthopnea. In these situations, the noninvasive vascular test may be performed with the patient in a more comfortable position, which may include sitting in a wheelchair with the leg in a dependent position. The objective of this investigation was to evaluate the effect of patient positioning on measurement of the digital pressure.

METHOD

Hallux pressures were measured in 20 healthy volunteers in 3 variable positions of limb dependency.

RESULTS

The mean±standard deviation of digital pressure for subjects while lying supine with the limb at heart level was 103.5±26.0 mmHg (the recommended position for performance of the test), while sitting upright with the limb level on the table was 130.6±27.9 mmHg (+26.2%, p<0.0001) and sitting upright with the limb in a dependent position was 169.8±30.8 mmHg (+64.1%, p<0.0001).

CONCLUSION

On the basis of these results, we conclude that patient positioning has a significant effect on measurement of digital pressure.

A novel quantitative method for diabetic cardiac autonomic neuropathy assessment in type 1 diabetic mice

In this work, we used a sensitive and noninvasive computational method to assess diabetic cardiovascular autonomic neuropathy (DCAN) from pulse oximeter (photoplethysmographic; PPG) recordings from mice. The method, which could be easily applied to humans, is based on principal dynamic mode (PDM) analysis of heart rate variability (HRV). Unlike the power spectral density, PDM has been shown to be able to separately identify the activities of the parasympathetic and sympathetic nervous systems without pharmacological intervention. HRV parameters were measured by processing PPG signals from conscious 1.5- to 5-month-old C57/BL6 control mice and in Akita mice, a model of insulin-dependent type 1 diabetes, and compared with the gold-standard Western blot and immunohistochemical analyses. The PDM results indicate significant cardiac autonomic impairment in the diabetic mice in comparison to the controls. When tail-cuff PPG recordings were collected and analyzed starting from 1.5 months of age in both C57/Bl6 controls and Akita mice, onset of DCAN was seen at 3 months in the Akita mice, which persisted up to the termination of the recording at 5 months. Western blot and immunohistochemical analyses also showed a reduction in nerve density in Akita mice at 3 and 4 months as compared to the control mice, thus, corroborating our PDM data analysis of HRV records. Western blot analysis of autonomic nerve proteins corroborated the PPG-based HRV analysis via the PDM approach. In contrast, traditional HRV analysis (based on either the power spectral density or time-domain measures) failed to detect the nerve rarefaction.

A review of signals used in sleep analysis

This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.