Kristian Solem

An efficient method for handling ectopic beats using the heart timing signal

The problem of analyzing heart rate variability in the presence of ectopic beats is revisited. Based on the integral pulse frequency modulation model and the closely related heart timing signal, a new technique is introduced which corrects for the occasional presence of ectopic beats. The correction technique, which involves the occurrence times of a certain number of beats preceding the ectopic beat, is computationally very efficient. From actual heart rate data, the results show that the new technique is associated with a much lower computational complexity (flops reduced by a factor of about 3000) than the original heart timing technique, while producing similar performance. It is also shown that the power spectrum and related clinical indices obtained by the new technique are more accurately estimated than by other methods.

Model-Based Detection of Heart Rate Turbulence

In this study, the integral pulse frequency modulation model is extended to account for the presence of ectopic beats and heart rate turbulence (HRT). Based on this model, a new statistical approach to the detection and characterization of HRT is presented. The detector structure involves a set of Karhunen-Loeve basis functions and a generalized likelihood ratio test statistic T(x) . The three most significant basis functions reflect the difference in heart rate prior to a ventricular ectopic beat (VEB) compared to after HRT, the ldquoaveragerdquo HRT, and a delayed contribution to HRT, respectively. Detector performance was studied on both simulated and ECG signals. Three different simulations were performed for the purpose of studying the influence of SNR, QRS jitter, and ECG sampling rate. The results show that the HRT test statistic T(x) performs better in all simulations than do the commonly used parameters known as turbulence onset (TO) and turbulence slope (TS). In order to attain the same performance as T(x), TS needs at least twice the amount of VEBs for averaging, and TO at least four times. The detector performance was also studied on ECGs acquired from eight patients who underwent hemodialysis treatment with the goal to discriminate between patients considered to be hypotension-resistant (HtR) and hypotension-prone (HtP). The results show that T(x) exhibits larger mean values in HtR patients than in HtP, suggesting that HRT is mostly present in HtR patients. The overlap between the two groups was larger for TO and TS than for T(x).

Prediction of Intradialytic Hypotension Using Photoplethysmography

Intradialytic hypotension is the most common acute complication during conventional hemodialysis treatment. Prediction of such events is highly desirable in clinical routine for prevention. This paper presents a novel prediction method of acute symptomatic hypotension in which the photoplethysmographic signal is analyzed with respect to changes in amplitude, reflecting vasoconstriction, and cardiac output. The method is based on a statistical model in which the noise is assumed to have Laplacian amplitude distribution. The performance is evaluated on 11 hypotension-prone patients who underwent hemodialysis treatment, resulting in seven events with acute symptomatic hypotension and 17 without. The photoplethysmographic signal was continuously acquired during treatment as was information on blood pressure and oxygen saturation. Using leave-one-out cross validation, the proposed method predicted six out of seven hypotensive events, while producing 1 false prediction out of 17 possible. The performance was achieved when the prediction threshold was chosen to be in the range 57%-65% of the photoplethysmographic envelope at treatment onset.

Central venous oxygen saturation and thoracic admittance during dialysis: new approaches to hemodynamic monitoring.

Intradialytic hypotension (IDH) is one of the most important short‐term complications to hemodialysis (HD). Inadequate cardiac filling due to a reduction in the central blood volume is believed to be a major etiological factor. The aim of this study was to evaluate whether these pathophysiologic events are reflected in the central venous oxygen saturation (ScO2) and thoracic admittance (TA) during dialysis. Twenty ambulatory HD patients, 11 hypotension prone (HP) and 9 hypotension resistant, with central vascular access, were monitored during 3 HD sessions each. ScO2, TA, finger blood pressure (BP), and relative change in blood volume (ΔBV) were measured and sampled continuously. The relative TA decrease and ΔBV were both largest in the HP group (p<0.05 for both), whereas ScO2 decreased only in HP patients (p<0.001). Baseline TA was lower in the HP group (p<0.01). Changes in ScO2 and TA correlated much closer than did changes in ScO2 and ΔBV (r=0.43 and 0.18, respectively). Our results suggest that an intradialytic decrease in cardiac output, as reflected by a fall in ScO2, is a common feature to HD patients prone to IDH. In patients using a central vascular access, ScO2 and TA measurements may be more specific to the pathophysiologic events preceding IDH than ΔBV—the current standard monitoring method.

An electrocardiogram-based method for early detection of abrupt changes in blood pressure during hemodialysis.

Clinical techniques for early detection of acute hypotension during conventional hemodialysis treatment are lacking, even though intradialytic hypotension is the most common acute complication. In this article, intradialytic hypotension is identified by means of signal analysis of data recorded at two clinics. The database consists of 30 treatments with concurrently acquired signals: the 12-lead electrocardiogram, continuous blood pressure, hematocrit, oxygen saturation, relative blood volume, and important hemodialysis variables. This article presents two characteristics, a heart rate turbulence (HRT) measure called turbulence slope (TS), and the LF/HF ratio, which provide information, at the beginning of hemodialysis treatment, on the patient’s propensity to hypotension (TS: p = 0.0038, and LF/HF ratio: p = 0.0028). The authors also present a novel dynamic echocardiography-based method for detecting intradialytic hypotension using complementary information on heart rate variability (HRV) and ectopic beat patterns. These two types of information reflect different mechanisms of cardiac activity. It is essential that both types are used for the detection of hypotension, because HRV analysis is inappropriate when several ectopic beats are present. The proposed dynamic echocardiography-based method offers early identification of the cases with acute intradialytic hypotension of the database.

Noninvasive Techniques for Prevention of Intradialytic Hypotension

Episodes of hypotension during hemodialysis treatment constitutes an important clinical problem which has received considerable attention in recent years. Despite the fact that numerous approaches to reducing the frequency of intradialytic hypotension (IDH) have been proposed and evaluated, the problem has not yet found a definitive solution-an observation which, in particular, applies to episodes of acute, symptomatic hypotension. This overview covers recent advances in methodology for predicting and preventing IDH. Following a brief overview of well-established hypotension-related variables, including blood pressure, blood temperature, relative blood volume, and bioimpedance, special attention is given to electrocardiographic and photoplethysmographic (PPG) variables and their significance for IDH prediction. It is concluded that cardiovascular variables which reflect heart rate variability, heart rate turbulence, and baroreflex sensitivity are important to explore in feedback control hemodialysis systems so as to improve their performance. The analysis of hemodialysis-related changes in PPG pulse wave properties hold considerable promise for improving prediction.

Prediction of hypotension in hemodialysis patients

Intradialytic hypotension (IDH) is the most common adverse complication during hemodialysis. Its early prediction and prevention will dramatically improve the quality of life for patients with an end stage renal disease. In a recent study, changes in the normalized envelope of the test statistic of the photoplethysmograpic (PPG) signal were found to predict acute symptomatic IDH. In the present study, the PPG-based predictor is generalized to include a patient-dependent threshold which incorporates on-line information on heart rate variability and heart rate turbulence. From datasets with patients prone and resistant to IDH, the results show that symptomatic IDH could be correctly predicted in 9 out of 14 cases, while 5 out of 24 were falsely predicted. In a subset of the data containing only patients prone to IDH, acute symptomatic IDH could be correctly predicted in 5 out of 5 cases, with one false prediction out of 14. When testing the robustness of the predictor, no significant changes were observed in the test statistic when controlled changes occurred in dialysis fluid temperature, ultrafiltration rate and body position.

Model-Based Detection of Heart Rate Turbulence Using Mean Shape Information

A generalized likelihood ratio test (GLRT) statistic is proposed for detection of heart rate turbulence (HRT), where a set of Karhunen-LoE¿ve basis functions models HRT. The detector structure is based on the extended integral pulse frequency modulation model that accounts for the presence of ectopic beats and HRT. This new test statistic takes a priori information regarding HRT shape into account, whereas our previously presented GLRT detector relied solely on the energy contained in the signal subspace. The spectral relationship between heart rate variability (HRV) and HRT is investigated for the purpose of modeling HRV ¿noise¿ present during the turbulence period, the results suggesting that the white noise assumption is feasible to pursue. The performance was studied for both simulated and real data, leading to results which show that the new GLRT detector is superior to the original one as well as to the commonly used parameter turbulence slope (TS) on both types of data. Averaging ten ventricular ectopic beats, the estimated detection probability of the new detector, the previous detector, and TS were found to be 0.83, 0.35, and 0.41, respectively, when the false alarm probability was held fixed at 0.1.

Detection of hypotension during hemodialysis using the ECG

There is today a lack of clinical techniques for detecting acute hypotension during conventional hemodialysis treatment, despite the fact that hypotension remains the - most common acute complication during hemodialysis. Hypotension is often followed by nausea, vomiting, and even fainting, not only strenuous for the patient but requires considerable attention from the nursing staf. The problem of detecting hypotension was studied by means of a multimodal database. The database consists of 30 treatments in which each treatment includes several simultaneously acquired signals. Acute .symptomatic hypotension occurred in 2 of the 30 treatments. An ECG-based method for detecting hypotension has been developed. n e method involves information on heart rate variability (HRV) and ectopic beat patterns. The proposed method does not only detect the two cases of acute hypotension but also provides information of the patients propensity to hypotension at an early stage of hemodialysis.

Extracting a Cardiac Signal From the Extracorporeal Pressure Sensors of a Hemodialysis Machine

Although patients undergoing hemodialysis treatment often suffer from cardiovascular disease, monitoring of cardiac rhythm is not performed on a routine basis. Without requiring any extra sensor, this study proposes a method for extracting a cardiac signal from the built-in extracorporeal venous pressure sensor of the hemodialysis machine. The extraction is challenged by the fact that the cardiac component is much weaker than the pressure component caused by the peristaltic blood pump. To further complicate the extraction problem, the cardiac component is difficult to separate when the pump and heart rates coincide. The proposed method estimates a cardiac signal by subtracting an iteratively refined blood pump model signal from the signal measured at the extracorporeal venous pressure sensor. The method was developed based on simulated pressure signals, and evaluated on clinical pressure signals acquired during hemodialysis treatment. The heart rate estimated from the clinical pressure signal was compared to that derived from a photoplethysmographic reference signal, resulting in a difference of 0.07 ± 0.84 beats/min. The accuracy of the heartbeat occurrence times was studied for different strengths of the cardiac component, using both clinical and simulated signals. The results suggest that the accuracy is sufficient for analysis of heart rate and certain arrhythmias.