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Seismocardiography and Machine Learning Algorithms to Assess Clinical Status of Patients with Heart Failure in Cardiopulmonary Exercise Testing

      Introduction

      Cardiopulmonary exercise testing (CPET) is an important risk stratification tool in patients (pts) with heart failure (HF); measures such as peak VO2, VE/VCO2 slope have prognostic value in HF pts to determine whether a patient needs advanced heart therapy or not. In our previous studies, we have shown that wearable chest patch based seismocardiogram (SCG) signals can be used to estimate features from CPET and SCG can be used to differentiate between compensated (C) and decompensated (D) pts with HF following exercise (6 minute walk test).

      Hypothesis

      We hypothesized that changes in SCG features from rest to peak exercise would be less in D pts compared to C pts, as D pts have less cardiovascular reserve to meet elevated cardiac demand during higher exercise intensity.

      Methods

      We conducted CPET using ramp bicycle in 6 C pts (50% had HF, 100% men, ejection fraction [EF] 0.61 ± 0.18) and 5 D pts (100% had HF, 40% men, EF 0.41 ± 0.29). SCG and ECG signals were simultaneously recorded using our custom-built wearable chest patch (Fig. 1a). We have segmented these signals into heart beats and averaged the beats with a moving average window and extracted features (amplitude, frequency and time domain) from the SCG. We combined the SCG features (beat-by-beat) using dimension reduction techniques and compared the changes in SCG features from rest to different exercise intensity levels, by calculating the distance of a distribution for a particular intensity to rest distribution. We have compared the changes in SCG with exercise intensity between C and D pts (classified based on CPET results).

      Results

      We found that normalized distance matrix (NDM) increased significantly (p<0.05) for C pts (Fig 1b & c) from anaerobic threshold (AT) to peak exercise and decreased significantly (p<0.05) into recovery (peak NDM of 1.91 ± 0.53 for VO2-max of 17.41 ± 5 mL/kg/min). Whereas for D pts it started a bit higher, increased in the first stage of exercise, and then showed little change (p>0.05) between AT and peak exercise and then decreased slightly (p>0.05) into recovery (peak NDM of 1.73 ± 0.16 for VO2-max of 9.83 ± 1.27 mL/kg/min). Difference in NDM from AT to peak exercise was higher (p=0.08) for C pts (0.29 ± 0.14) compared to D pts (0.09 ± 0.13).

      Conclusions

      Wearable SCG and ECG measurements can be used to assess clinical status by monitoring hemodynamic responses to exercise. This wearable device can potentially be used as a risk stratifying device in pts with HF by tracking hemodynamic parameters in daily activities.
      Figure 1.
      Figure 1
      Figure 1(a) Image showing the wearable patch for measuring ECG and SCG signals from the chest simultaneously with CPET, corresponding wearable signals and data processing block diagram for wearable and CPET data. (b) Trend of VO2 and (c) SCG features at different levels of exercise intensity. Rest=baseline, UC=unloaded cycling, AT=anaerobic threshold, Max=peak exercise, R30=R30s into recovery, R60=60s into recovery, Comp = compensated pts, Decomp = decompensated pts. (*P<0.05 intragroup between exercise intensity for compensated subjects only, ‡P<0.01 intergroup at a particular exercise intensity).