Docs · References

Referências

References

Bibliografia, normas regulatórias aplicáveis, datasets públicos utilizados para calibração estatística e ferramentas de software de referência.

Bibliography, applicable regulatory standards, public datasets used for statistical calibration, and reference software tools.

Modelagem bioelétrica

Bioelectrical modeling

  1. Cole, K. S.; Cole, R. H. Dispersion and absorption in dielectrics I. Alternating current characteristics. J. Chem. Phys. 9, 341 (1941). → Modelo Cole-Cole; base dos módulos 01-02.
  2. Schwan, H. P. Electrical properties of tissue and cell suspensions. Adv. Biol. Med. Phys. 5, 147–209 (1957). → Dispersões α, β, γ; justificativa para fc no MHz.
  3. Kyle, U. G. et al. Bioelectrical impedance analysis – part I: review of principles and methods. Clin. Nutr. 23, 1226–1243 (2004). → Configuração tetrapolar; protocolo padrão.
  4. Bera, T. K. Bioelectrical impedance methods for noninvasive health monitoring: a review. J. Med. Eng. 2014, 381251 (2014). → Survey de configurações 4/8/12 eletrodos.

ICG e plethismografia

ICG and plethysmography

  1. Kubicek, W. G. et al. Development and evaluation of an impedance cardiac output system. Aerosp. Med. 37, 1208–1212 (1966). → Kubicek ICG; base do módulo 04.
  2. Sramek, B. B. Noninvasive technique for measurement of cardiac output by means of electrical impedance. Proc. 5th ICEBI, 39–42 (1981).
  3. Bernstein, D. P. Continuous noninvasive real-time monitoring of stroke volume and cardiac output by thoracic electrical bioimpedance. Crit. Care Med. 14, 898–901 (1986).
  4. Charlton, P. H. et al. Breathing rate estimation from the electrocardiogram and photoplethysmogram: a review. IEEE Rev. Biomed. Eng. 11, 2–20 (2018). → Algoritmos de extração de FR.

Gasto energético e fusão

Energy expenditure and fusion

  1. Keytel, L. R. et al. Prediction of energy expenditure from heart rate monitoring during submaximal exercise. J. Sports Sci. 23, 289–297 (2005). → Equação Keytel; módulo 03.
  2. Brage, S. et al. Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure. J. Appl. Physiol. 96, 343–351 (2004).
  3. Plasqui, G.; Westerterp, K. R. Physical activity assessment with accelerometers: an evaluation against doubly labeled water. Obesity 15, 2371–2379 (2007).
  4. Schmidt, P. et al. Introducing WESAD, a multimodal dataset for wearable stress and affect detection. Proc. 20th ACM ICMI, 400–408 (2018). → Dataset WESAD; calibração do módulo 06.

Processamento digital de sinais

Digital signal processing

  1. Oppenheim, A. V.; Schafer, R. W. Discrete-Time Signal Processing. 3ª ed., Pearson (2009). → Filtros IIR; ponto-fixo.
  2. Pan, J.; Tompkins, W. J. A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME-32, 230–236 (1985). → Detector de picos; arquitetura do módulo 05.
  3. Smith, S. W. The Scientist and Engineer's Guide to Digital Signal Processing. California Technical Pub (1997). → Referência aberta · dspguide.com.

Normas regulatórias

Regulatory standards

  1. IEC 62304:2006/AMD1:2015 · Medical device software — Software life cycle processes. → Classe B aplicável.
  2. IEC 60601-1:2005/AMD2:2020 · Medical electrical equipment — Part 1: General requirements for basic safety and essential performance.
  3. IEC 60601-2-49:2011 · Particular requirements for multifunction patient monitoring equipment.
  4. ISO 14971:2019 · Medical devices — Application of risk management.
  5. ANVISA RDC 657/2022 · Registro e notificação de dispositivos médicos.
  6. FDA 21 CFR 870.2700 e 21 CFR 870.2840 · Oximeter; impedance plethysmograph. → Precedente 510(k).
  7. HL7 FHIR R5 · Fast Healthcare Interoperability Resources. → Integração SUS Digital.

Datasets públicos

Public datasets

  1. MIMIC-III Waveform Database · Goldberger, A. L. et al. PhysioBank, PhysioToolkit, and PhysioNet. Circulation 101, e215–e220 (2000). → physionet.org/content/mimic3wdb/
  2. WESAD · Wearable Stress and Affect Detection. → PPG + accel + respiração.
  3. PPG-DaLiA · Reiss, A. et al. Deep PPG: large-scale heart rate estimation with convolutional neural networks. Sensors 19, 3079 (2019).
  4. PAMAP2 · Reiss, A.; Stricker, D. Introducing a new benchmarked dataset for activity monitoring. 16th ISWC, 108–109 (2012).

Aviso de uso · Os datasets foram referenciados estatisticamente (médias, desvios, amplitudes de artefatos) para parametrizar os geradores sintéticos do módulo 06. Nenhum dado real foi embutido. Uso direto em TRL 4 requer credenciais PhysioNet (CITI training) e aceitação dos termos.

Usage notice · Datasets were referenced statistically (means, deviations, artifact amplitudes) to parameterize the module 06 synthetic generators. No real data was embedded. Direct use at TRL 4 requires PhysioNet credentials (CITI training) and acceptance of terms.

Ferramentas de referência

Reference tools

  • NeuroKit2 · Makowski, D. et al. NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods 53, 1689–1696 (2021).
  • WFDB Python · github.com/MIT-LCP/wfdb-python. → Loader PhysioNet · TRL 4.
  • SciPy Signal · Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).