Fibromyalgia syndrome (FMS) is identified by widespread musculoskeletal pain, sleep disturbance, nonrestorative sleep, fatigue, morning stiffness and anxiety. Anxiety is very common in Fibromyalgia and generally leads to a misdiagnosis. Self-rated Beck Anxiety Inventory (BAI) and doctor-rated Hamilton Anxiety Inventory (HAM-A) are frequently used by specialists to determine anxiety that accompanies fibromyalgia. However, these semi-quantitative anxiety tests are still subjective as the tests are scored using doctor-rated or self-rated scales.
In this study, we investigated the relationship between heart rate variability (HRV) frequency subbands and anxiety tests. The study was conducted with 56 FMS patients and 34 healthy controls. BAI and HAM-A test scores were determined for each participant. ECG signals were then recruited and 71 HRV subbands were obtained from these ECG signals using Wavelet Packet Transform (WPT). The subbands and anxiety tests scores were analyzed and compared using multilayer perceptron neural networks (MLPNN).
The results show that a HRV high frequency (HF) subband in the range of 0.15235Hz to 0.40235Hz, is correlated with BAI scores and another HRV HF subband, frequency range of 0.15235Hz to 0.28907Hz is correlated with HAM-A scores. The overall accuracy is 91.11% for HAM-A and 90% for BAI with MLPNN analysis.
Doctor-rated or self-rated anxiety tests should be supported with quantitative and more objective methods. Our results show that the HRV parameters will be able to support the anxiety tests in the clinical evaluation of fibromyalgia. In other words, HRV parameters can potentially be used as an auxiliary diagnostic method in conjunction with anxiety tests.